2017 puget sound regional travel studydraft final report 2017 puget sound regional travel study...

DRAFT FINAL REPORT

2017 PUGET SOUND REGIONAL TRAVEL STUDY

3.29.2018

2200 Wilson Boulevard, Suite 410 Arlington, VA 22201-3324

888.774.5986 www.rsginc.com

PREPARED FOR:

PUGET SOUND REGIONAL COUNCIL

SUBMITTED BY:

RSG

2017 PUGET SOUND REGIONAL TRAVEL STUDY

PREPARED FOR: PUGET SOUND REGIONAL COUNCIL

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CONTENTS

ACKNOWLEDGEMENTS ............................................................................................................................. 1

Study Sponsors ...................................................................................................................................... 1

Consultant Team .................................................................................................................................... 1

GLOSSARY OF TERMS .............................................................................................................................. 2

1.0 INTRODUCTION ............................................................................................................................... 4

1.1 | Study Objectives ............................................................................................................................ 4

1.2 | Study Area ..................................................................................................................................... 4

1.3 | Study Timeline ............................................................................................................................... 6

2.0 SURVEY SAMPLING ....................................................................................................................... 7

2.1 | Sampling Goals ............................................................................................................................. 7

2.2 | Sampling Methods ......................................................................................................................... 7

Sampling Frame ............................................................................................................................... 7

Stratification ..................................................................................................................................... 7

Sample Rates ................................................................................................................................. 10

2.3 | Sample Monitoring ....................................................................................................................... 12

3.0 SURVEY DESIGN ........................................................................................................................... 13

3.1 | Overview ...................................................................................................................................... 13

3.2 | Survey Stages and Participation Methods .................................................................................. 13

Participation Group Assignments ................................................................................................... 13

Travel Date Assignments ............................................................................................................... 13

Study Components ......................................................................................................................... 13

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Language Options .......................................................................................................................... 14

3.3 | Survey Incentives ........................................................................................................................ 14

3.4 | Household, Person, and Vehicle Data Collected ........................................................................ 15

3.5 | Travel Diary Data Collected ........................................................................................................ 16

Trip Data ........................................................................................................................................ 16

Travel Day Data ............................................................................................................................. 18

Child Reporting .............................................................................................................................. 18

3.6 | Additional Data Collected ............................................................................................................ 19

3.7 | Survey Design Updates ............................................................................................................... 19

Structural Changes ........................................................................................................................ 19

Content Changes: Added/Modified Questions ............................................................................... 20

Content Changes: Dropped Questions .......................................................................................... 20

4.0 SURVEY BRANDING, COMMUNICATION, AND ADMINISTRATION ......................................... 21

4.1 | Study Branding ............................................................................................................................ 21

4.2 | Study Invitation Materials ............................................................................................................ 21

4.3 | Study Website ............................................................................................................................. 23

4.4 | Public Outreach ........................................................................................................................... 24

Target Audiences ........................................................................................................................... 24

Outreach Actions ............................................................................................................................ 24

Facebook Advertisement ............................................................................................................... 25

4.5 | Participant Support ...................................................................................................................... 28

Outbound Participant Support ........................................................................................................ 28

Inbound Participant Support .......................................................................................................... 29

5.0 DATASET PREPARATION ............................................................................................................ 31

5.1 | Overview ...................................................................................................................................... 31

5.2 | Dataset Preparation ..................................................................................................................... 31

Database Setup and Real-Time Quality Controls .......................................................................... 31

Geographic Data Checks ............................................................................................................... 32

GPS Data Review and Quality Controls ........................................................................................ 33

Derivation of Trips for Nonparticipating Household Members ....................................................... 33

Income Imputation .......................................................................................................................... 34

Integration of Data from Multiple Retrieval Modes ......................................................................... 34

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Completion and Exclusion Criteria ................................................................................................. 35

6.0 EXPANSION AND WEIGHTING .................................................................................................... 37

6.1 | Role of Weighting ........................................................................................................................ 37

6.2 | Step 1. Expansion Based on Sampling Probabilities .................................................................. 37

6.3 | Weighting Targets ....................................................................................................................... 38

6.4 | Step 2. Reweighting Survey Households to Expansion Targets ................................................. 41

6.5 | Step 3. Trip Weights and Trip Correction factors ........................................................................ 42

Trip Rate Adjustment ..................................................................................................................... 47

6.6 | Final Weights and Recommended Use ....................................................................................... 50

7.0 SURVEY RESULTS ........................................................................................................................ 52

7.1 | Survey Response Rates .............................................................................................................. 52

7.2 | Household Results ...................................................................................................................... 55

7.3 | Person Results ............................................................................................................................ 57

7.4 | Trip Rates and Travel Analysis Results ...................................................................................... 60

Trip Rates ....................................................................................................................................... 61

At Home Activities .......................................................................................................................... 64

Travel Analysis ............................................................................................................................... 66

8.0 FUTURE CONSIDERATIONS ........................................................................................................ 72

8.1 | Summary ..................................................................................................................................... 72

8.2 | Future considerations .................................................................................................................. 72

9.0 APPENDICES ................................................................................................................................. 73

9.1 | Questionnaires ............................................................................................................................ 73

9.2 | Invitation Materials ....................................................................................................................... 73

List of Figures

FIGURE 1: MAP OF STUDY AREA (PROVIDED BY PSRC) ....................................................................................... 5

FIGURE 2: STUDY COMPONENTS AND GROUP ASSIGNMENTS .......................................................................... 14

FIGURE 3: PRIMARY WORKPLACE LOCATION GEOCODER (RECRUIT SURVEY SCREENSHOT) ................... 16

FIGURE 4: RSURVEY TRIP ROSTER (SCREENSHOT) ............................................................................................ 17

FIGURE 5: RMOVE TRIP ROSTER (SCREENSHOT) ................................................................................................ 18

FIGURE 6: 2017 STUDY LOGO .................................................................................................................................. 21

FIGURE 7: EXAMPLE SURVEY POSTCARD (FRONT) ............................................................................................. 22

FIGURE 8: EXAMPLE SURVEY POSTCARD (BACK) ............................................................................................... 22

FIGURE 9: PROJECT WEBSITE HOME PAGE ......................................................................................................... 24

FIGURE 10: FACEBOOK ADVERTISEMENT ............................................................................................................ 26

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FIGURE 11: DISTRIBUTION OF FINAL HOUSEHOLDS WEIGHTS .......................................................................... 42

FIGURE 12: PUMS-BASED TARGETS FOR EACH PUMA GROUP AND TARGET VARIABLE ............................. 42

FIGURE 13: PARTICIPANT HOME LOCATIONS, BY PARTICIPATION GROUP (RMOVE HOUSEHOLDS ON

LEFT, RSURVEY HOUSEHOLDS ON RIGHT) .................................................................................................. 52

FIGURE 14: ONLINE SHOP TIME BY DAY OF WEEK, WEIGHTED (GROUP 1) ..................................................... 65

FIGURE 15: ONLINE SHOP TIME BY DAY OF WEEK, WEIGHTED (GROUP 2) ..................................................... 65

FIGURE 16: TELEWORK TIME BY DAY OF WEEK, WEIGHTED (GROUP 1).......................................................... 66

FIGURE 17: TELEWORK TIME BY DAY OF WEEK, WEIGHTED (GROUP 2).......................................................... 66

FIGURE 18: TRIP DESTINATION PURPOSE (WEIGHTED) ...................................................................................... 67

FIGURE 19: TRIP DESTINATION PURPOSE BY HOUSEHOLD INCOME (WEIGHTED) ......................................... 67

FIGURE 20: TRIP MODE BY HOUSEHOLD INCOME (WEIGHTED) ......................................................................... 68

FIGURE 21: TRIP MODE BY AGE GROUP(WEIGHTED) .......................................................................................... 69

FIGURE 22: TRIPS BY TIME OF DAY (WEIGHTED) ................................................................................................. 69

FIGURE 23: TRIPS BY TIME OF DAY AND DESTINATION PURPOSE (WEIGHTED) ............................................. 70

FIGURE 24: TRIPS BY DISTANCE (WEIGHTED) ...................................................................................................... 71

FIGURE 25: TRIPS BY DISTANCE AND TRIP MODE (WEIGHTED) ........................................................................ 71

List of Tables

TABLE 1: STUDY TIMELINE ........................................................................................................................................ 6

TABLE 2: ORIGINAL AND ADJUSTED TARGET SAMPLE SIZES BY SUB-REGION ............................................... 8

TABLE 3: 2017 SAMPLING SEGMENTS AND SAMPLE RATES ............................................................................. 11

TABLE 4: PERFORMANCE BY AGE ......................................................................................................................... 27

TABLE 5: PERFORMANCE BY DEVICE .................................................................................................................... 28

TABLE 6: INBOUND E-MAIL TOPICS ........................................................................................................................ 30

TABLE 7: RESULTS OF INITIAL EXPANSION SUMMARY ...................................................................................... 37

TABLE 8: RESULTS OF INITIAL EXPANSION WITHIN SAMPLING SEGMENTS ................................................... 38

TABLE 9: PUMA HH TOTAL SCALE FROM ACS 2016 TO OFM 2017 ESTIMATES ............................................... 40

TABLE 10: PUMA HH TOTAL SCALE FROM ACS 2016 TO OFM 2017 ESTIMATES ............................................. 41

TABLE 11: NUMBER OF PERSON-DAYS AND AVERAGE LINKED TRIPS PER DAY, BY SURVEY METHOD

AND DAY OF WEEK........................................................................................................................................... 43

TABLE 12: ANALYSIS OF TRIP RATES AND TRAVEL PATTERNS, BY RESPONDENT GROUP ......................... 45

TABLE 13: PERCENTAGE OF TRIPS BY OD PURPOSE/MODE/DISTANCE COMBINATION AND RESPONDENT

TYPE (DATA WEIGHTED BY HOUSEHOLD-LEVEL WEIGHTS) ..................................................................... 48

TABLE 14: AVERAGE TRIPS BY PERSON-DAY BY OD PURPOSE/MODE/DISTANCE COMBINATION AND

RESPONDENT TYPE ......................................................................................................................................... 49

TABLE 15: AVERAGE TRIPS BY PERSON-DAY BY OD PURPOSE/MODE/DISTANCE COMBINATION AND

RESPONDENT TYPE AFTER ADJUSTMENT ................................................................................................... 49

TABLE 16: NUMBERS OF HOUSEHOLDS, UNWEIGHTED AND WEIGHTED ........................................................ 50

TABLE 17: NUMBERS OF PERSONS, UNWEIGHTED AND WEIGHTED ................................................................ 50

TABLE 18: NUMBERS OF PERSON-DAYS, UNWEIGHTED AND WEIGHTED ....................................................... 51

TABLE 19: NUMBERS OF WEEKDAY TRIPS, UNWEIGHTED AND WEIGHTED .................................................... 51

TABLE 20: NUMBERS OF WEEKDAY TRIPS PER PERSON-DAY, UNWEIGHTED AND WEIGHTED ................... 51

TABLE 21: HOUSEHOLDS BY PARTICIPATION GROUP ........................................................................................ 53

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TABLE 22: SURVEY RESPONSE SUMMARY ........................................................................................................... 53

TABLE 23: HOUSEHOLD SURVEY RESPONSE BY SAMPLE SEGMENT .............................................................. 54

TABLE 24: HOUSEHOLD SIZE, BY PARTICIPATION GROUP (UNWEIGHTED)..................................................... 55

TABLE 25: HOUSEHOLD SIZE, BY PARTICIPATION GROUP (WEIGHTED) .......................................................... 55

TABLE 26: HOUSEHOLDS BY INCOME GROUPAND PARTICIPATION GROUP (UNWEIGHTED) ....................... 56

TABLE 27: HOUSEHOLDS BY INCOME GROUP AND PARTICIPATION GROUP WITH INCOME IMPUTATION

(WEIGHTED) ....................................................................................................................................................... 56

TABLE 28: HOUSEHOLDS BY VEHICLES AND PARTICIPATION GROUP (UNWEIGHTED) ................................. 57

TABLE 29: HOUSEHOLDS BY VEHICLES AND PARTICIPATION GROUP (WEIGHTED) ...................................... 57

TABLE 30: PERSONS BY AGE GROUP AND PARTICIPATION GROUP (UNWEIGHTED) .................................... 58

TABLE 31: PERSONS BY GENDER AND PARTICIPATION GROUP (UNWEIGHTED) ........................................... 58

TABLE 32: PERSONS (ADULTS ONLY) BY RACE/ETHNICITY AND PARTICIPATION GROUP (UNWEIGHTED) –

SELECT ALL THAT APPLY QUESTION ........................................................................................................... 59

TABLE 33: PERSONS BY EMPLOYMENT STATUS AND PARTICIPATION GROUP (UNWEIGHTED) .................. 60

TABLE 34: PERSONS BY STUDENT TYPE AND PARTICIPATION GROUP (UNWEIGHTED) ............................... 60

TABLE 35: NUMBERS OF HOUSEHOLDS, PERSONS AND TRIPS BY PARTICIPANT GROUP (WEIGHTED). ... 61

TABLE 36: NUMBERS OF HOUSEHOLDS, PERSONS AND TRIPS BY SURVEY YEAR (WEIGHTED) ................. 62

TABLE 37: HOUSEHOLD WEEKDAY TRIP RATES BY INCOME ............................................................................ 62

TABLE 38: HOUSEHOLD TRIP RATES BY HOUSEHOLD SIZE (GROUPS 1 & 2) .................................................. 63

TABLE 39: PERSON TRIP RATES BY AGE GROUP ................................................................................................ 63

TABLE 40: PERSON TRIP RATES BY EMPLOYMENT STATUS ............................................................................. 64

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ACKNOWLEDGEMENTS

The 2017 Puget Sound Regional Travel Study was supported by the efforts and advice of many partners.

The Puget Sound Regional Council offers their thanks and appreciation to these agencies and their staff

for their input on this study.

STUDY SPONSORS

• Puget Sound Regional Council

• City of Redmond

• City of Seattle

CONSULTANT TEAM

• RSG (Prime Consultant)

• ETC Institute (Sub-Consultant)

Puget Sound Regional Council 2017 Puget Sound Regional Travel Study

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GLOSSARY OF TERMS

TERM DEFINITION

ABS: Address-based sampling (ABS) draws from a complete list of households within a given

geographic area. This study’s sampling frame was the full list of addresses in the specified

Census Block Groups (BGs) as available from the United States Postal Service (USPS)

Computerized Delivery Sequence File.

ACS: The American Community Survey (ACS) is an ongoing U.S. Census Bureau survey that

gathers demographic and other person- and household-level information. ACS estimates

informed this study’s sampling and weighting methods.

BG: A Block Group (BG) is a statistical division of a census tract and a contiguous geographic

area that typically contains 600–3,000 people.

GPS: This study included a smartphone component that collected Global Positioning System

(GPS) coordinates from participants’ smartphone devices. GPS is a satellite system that

collects both time and location (latitudinal and longitudinal) points.

Group: In the context of this study, a “group” refers to the mode through which households

completed the travel diary portion of the study. Group 1 households completed Part 2 using

rMove (a smartphone app), and Group 2 households completed Part 2 using rSurvey (an

online survey platform). Groups were not assigned until each household completed Part 1

(the demographic and household information section).

HH: In this study, a household (HH) encompassed “anyone who lives in the home, including

roommates, relatives, friends, and household help.”

HTS: A household travel survey (HTS) is a periodic survey that collects trip and other travel

information from an entire household for a predefined period (at least one full day).

PSRC: The Puget Sound Regional Council (PSRC) “is a regional planning agency with specific

responsibilities under federal and state law for transportation planning, economic

development and growth management.”1

RGC: The Puget Sound region includes 29 Regional Growth Centers (RGCs), which are “locations

of the region’s most significant business, governmental, and cultural facilities and are

planning for growth.”2

1 Source: https://www.psrc.org/about/what-we-do 2 Source: https://www.psrc.org/centers

https://www.psrc.org/about/what-we-do

https://www.psrc.org/centers

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TERM DEFINITION

rMove: rMove™ is a smartphone app designed to collect complete household travel diary

information from invited participants. The app is compatible with most Android and iOS

phones that are less than four years old. The study was designed to allow approximately

20% of participants to complete Part 2 using rMove.

rSurvey: rSurvey™ is an online travel survey platform designed to collect complete household travel

diary information from invited participants. All participants completed Part 1 using rSurvey,

and approximately 80% of participants completed Part 2 of the study using rSurvey.

Travel

date:

In the context of this study, a “travel date” is the first (or only) day on which a household

reported its trips.

UV: The City of Seattle has designated 41 areas as urban villages (UV). These are “areas where

conditions can best support increased density needed to house and employ the city’s

newest residents.”3

3 Source: https://www.seattle.gov/Documents/Departments/OPCD/OngoingInitiatives/SeattlesComprehensivePlan/UrbanVillageElement.pdf

https://www.seattle.gov/Documents/Departments/OPCD/OngoingInitiatives/SeattlesComprehensivePlan/UrbanVillageElement.pdf


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1.0 INTRODUCTION

1.1 | STUDY OBJECTIVES

The 2017 Puget Sound Regional Travel Study followed the 2014–2015 Puget Sound Regional Travel

Study and was the start of a planned six-year data collection effort that will likely include two additional

data collection waves in 2019 and 2021. The 2017 study, like the most recent studies, collected

household- and person-level activity and travel pattern information from residents throughout the Puget

Sound Regional Council (PSRC) four-county region.

The overarching goal of the multiyear program is to maintain an updated source of household travel

behavior data that:

• supports modeling and planning needs,

• facilitates trend analysis over time, and

• allows for regular study design updates to integrate evolving data collection methods and

emerging travel behaviors and transportation issues.

1.2 | STUDY AREA

Consistent with recent surveys, the 2017 study encompassed the entire PSRC four-county region, which

includes King, Kitsap, Pierce, and Snohomish counties. The region includes 82 cities and towns with a

total population of over four million people. The study area comprises approximately 1,495,642

households.4

4 Estimated residential household population from ACS 2011–2015 five-year estimates.

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FIGURE 1: MAP OF STUDY AREA (PROVIDED BY PSRC)


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1.3 | STUDY TIMELINE

The scope of work for this project included both the design and administration of a one-day household

travel diary (approximately 80% of households before data cleaning) and a seven-day smartphone GPS

diary (approximately 20% of households before data cleaning). Table 1 documents the project’s schedule.

TABLE 1: STUDY TIMELINE

PHASE TIMELINE

Scope Refinement Jan. 2017–Feb. 2017

Survey Design Feb. 2017–Apr. 2017

Survey Implementation Apr. 2017–June 2017

Data Processing and Cleaning June 2017–Dec. 2017

Documentation June 2017–March 2018

Data Analysis and Weighting Dec. 2017–March 2018

Project Closure March 2018

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2.0 SURVEY SAMPLING

2.1 | SAMPLING GOALS

The 2017 study aimed to sample 3,100 complete households, which equates to a 0.21% sample rate

(based on data from the 2011–2015 American Community Survey (ACS)). This sample goal included

targets for the three sponsoring agencies:

• PSRC: 1,100 complete households in the four-county study region.

• City of Redmond: 450* complete households in Redmond.

• City of Seattle: 1,550* complete households in Seattle’s urban villages (UVs).

(*These samples exclude any households in those cities surveyed in the core regional sample.)

Typical sample rates for similar studies range from approximately 0.5–1%. The combined sample rate will

likely fall within this typical range because the 2017 study was only the first data collection wave of a

planned three-wave study—the goal is to combine data across multiple waves. By comparison, the 2014

study had a sample rate of approximately 0.6%. The sections below further explain the process RSG

used to determine the final sample rates for each Census BG.

2.2 | SAMPLING METHODS

SAMPLING FRAME

The primary sampling frame was the list of all households in the four-county study region (King, Kitsap,

Pierce, and Snohomish counties)5. RSG used address-based sampling (ABS) to select and invite

households to participate in the study. ABS involves drawing a random sample of addresses from all the

residential addresses in each defined geography such that all households in each defined geography

have an equal chance of selection for the sample. RSG purchased the final household mailing addresses

from Marketing Systems Group, which maintains the Computer Delivery Sequence file from the USPS.

STRATIFICATION

RSG first established the target sample sizes for each region. This was done to achieve the sampling

goals for each of the sponsoring agencies. The contracted sample sizes were determined within and by

each agency based on long-term data needs; however, to simplify the sampling approach in these

overlapping sub-regions (with part of PSRC’s sample coming from the City of Redmond and the Seattle

UVs), RSG grouped the proportion of PSRC’s sample expected in the Redmond and Seattle sample sub-

regions with the samples for those sub-regions.6 Table 2 shows the contracted and adjusted sample sizes

for each region.

5 The sampling frame was defined and stratified using ACS estimates of number of households in each Census block group; based on these ACS estimates, 17 block groups with no households or very few households (fewer than 33) were excluded from the sample analysis and final sample frame. 6 RSG assumed that the PSRC sample should be allocated proportionally to the number of households in ACS 2011–15, so 7.9% (87 out of 1,100) were in the Seattle UVs and 1.7% (19 out of 1,100) in Redmond.


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TABLE 2: ORIGINAL AND ADJUSTED TARGET SAMPLE SIZES BY SUB-REGION

SUB-REGION CONTRACTED SAMPLE SIZE

TOTAL HHs, ACS 2011-15

% OF REGIONAL

HHs

HHs IN THE PSRC

SAMPLE

ADJUSTED TARGET

SAMPLE SIZE

City of Redmond 450 24,398 1.6% 18 468

City of Seattle UVs 1,550 117,421 7.9% 87 1,637

Rest of PSRC 1,100 1,353,823 90.4% 995 995

TOTAL 3,100 1,495,642 100% 1,100 3,100

Once the total sample targets were finalized for each sub-region, the sample targets within each region

were stratified to achieve each agency’s objectives. Strata were defined using BGs and ACS data.

“Rest of PSRC” Stratification

Following the same approach as in 2014–2015, the ABS plan for the 2017 study included a combination

of simple geographic proportional sampling along with “targeted oversampling” (sampling at higher rates

in selected geographic areas of interest) and “compensatory sampling” (sampling at higher rates

according to the expected response rates in different BGs). Oversampling efforts were targeted at areas

with the highest presence of household types and travel behaviors that are typically underrepresented or

rarely found, but of interest for policy and planning goals. By targeting these areas for oversampling, the

likelihood of reaching the target demographics increased. The variables considered for oversampling

included low-income households, households without vehicles, non-auto commuters, and renter

households with young heads of household.7 These variables are often spatially correlated across BGs—

a high proportion of one or two variables means an increased likelihood that other variables are higher.

However, it was also important to identify BGs that were high in one variable, but not others, to improve

analysis. (For example, BGs with high numbers of zero-vehicle households that are not located in

urban/accessible areas.) Additionally, as in 2014–2015, the sample objectives included oversampling in

PSRC’s Regional Growth Centers (RGCs).

To achieve the sampling objective, RSG identified BGs eligible for oversampling. The identification

process in the 2017 study used nearly the same logic as in the 2014–2015 study. The “targeted

oversample” segments included any BG that met any one of the following criteria, based on data from the

2011–2015 ACS:

• BGs designated as part of an RGC.

• BGs where 35% or more of HHs have income less than $25,000.

• BGs where 20% or more of HHs do not own a vehicle.

• BGs where 40% or more of workers do not commute by car.

• BGs where 40% or more of HHs are renters with head of household under age 35.

These criteria were based on analysis of these ACS variables which evaluated the concentration of these

characteristics; block groups with the highest concentration of these variables of interest were chosen.

Specifically, block groups that were approximately in the 90th percentile or above for a given characteristic

were chosen for oversampling. (For example, when ranking block groups by the proportion of low-income

7 The focus on young renter households is slightly different from 2014-2015, when the focus was on young one-person households, as one-person households tend to have high response rates.

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households, RSG found that in the top 10% of block groups, 35% or more households had incomes less

than $25,000). Once the PSRC region BGs were divided into “regular” and “targeted oversample”

segments, RSG set sample targets for each of these segments. As was done in the 2014–2015 study,

RSG recommended a sample rate in the “oversample” segment that was 2.5 times that of the “regular”

sample (or 0.15% for the “oversample” segment and 0.06% for the “regular” segment). This

recommendation was based on the assessment of desired sample sizes (and expected results based on

ACS data) for these variables of interest. For example, to analyze and model travel behaviors for zero-

vehicle households, the desired sample size from block groups with more than 20% zero-vehicle

households should be large enough to collect a statistically significant sample of these households.

These segments were then further divided into response rate groups (low, medium, and high response) to

conduct “compensatory oversampling,” which produced six sampling segments. Response rates for each

BG were predicted using a model developed with ACS data and response rates from previous surveys;8

this model indicated which BGs were more likely to have “low,” “medium,” or “high” response rates. These

predicted response rates were then reduced slightly to provide more conservative estimates.

“City of Redmond” Stratification

The sampling plan for the City of Redmond was designed using the same logic as for the “Rest of PSRC”

(using the same variables and targets for targeted oversampling and using the same model to predict

response rates for compensatory oversampling). The main difference was that two BGs designated as

“downtown” Redmond were singled out for higher oversampling. A primary objective for the City of

Redmond was to obtain approximately half of their sample from their downtown area (composed of two

BGs). Based on the expected response rates and total number of households in downtown Redmond, the

desired sample size was determined to be difficult to achieve. Thus, RSG and Redmond decided to invite

100% of households in the downtown BGs to maximize the number of possible responses. The city also

conducted additional recruitment and outreach activities to encourage response (discussed in Section

4.0). The sample in Redmond was stratified into seven segments using the downtown sample and the

targeted and compensatory oversampling. (Two of these potential segments— “low-response” groups in

the regular and downtown BGs—did not contain any households.) Like PSRC, the predicted response

rates were reduced slightly to be more conservative.

City of Seattle Urban Villages (UV) Stratification

The City of Seattle requested a different type of sampling strategy. The 41 UVs within the city’s sample

were split into three types: 1) Urban Centers; 2) Hub UVs; and 3) Residential UVs (also including the two

manufacturing/industrial centers). A “minimum target” was set for each type of UV—40 complete

households for the Urban Centers, 30 for the Hub UVs, and 20 for the Residential UVs. The sum of these

targets was 1,180, leaving another 460 households to reach the total target of 1,640 for this segment.

These were distributed in proportion to the number of households living in each UV to set the final targets.

Once the sample target for each UV was set, the same model was used to predict response rates and

invitation needs (again adjusting down to be conservative). The number of invitations needed in each UV

to meet the final targets was then estimated based on the predicted response rates. To reduce the

8 Bradley, et al. “Predicting and Applying Differential Response Rates in Address-Based Sampling for a Household Travel Survey,” V. 2526 of the Transportation Research Record (2015)


10

number of total sub-segments, the UVs were then grouped into ten categories, based on the (rounded-

up) percentage of addresses in those BGs that were purchased.9 The categories were 100%, 90%, 75%,

60%, 50%, 35%, 30%, 25%, 15%, and 10%. RSG separately monitored the 41 UVs, even though they

were grouped for address purchasing.

SAMPLE RATES

Table 3 includes the final sampling segments and sample rates.

9 Two UVs (South Park and University Campus) were estimated to require invitations to more than 100% of residential households, based on population size, sample target, and predicted response rates. These UVs were grouped with the “100%” invitation category.

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TABLE 3: 2017 SAMPLING SEGMENTS AND SAMPLE RATES

(SEGMENT) HHs (ACS 2011–2015)

TARGET SAMPLE RATE

TARGET SAMPLE SIZE

PREDICTED RESPONSE

RATE

# INVITES

PSRC REGULAR

(01) low response rate 248,641 0.06% 149 4.35% 3,440

(02) med. response rate 424,662 0.06% 255 5.70% 4,510

(03) high response rate 420,263 0.06% 252 7.60% 3,330

PSRC OVERSAMPLE


(12) med. response rate 46,145 0.15% 69 5.70% 1,220


PSRC Total 1,353,823 -- 1,046 -- 19,150

REDMOND REGULAR

(22) med. response rate 3,868 1.10% 43 5.70% 750


REDMOND OVERSAMPLE


(32) med. response rate 1,331 2.75% 37 5.70% 650

(33) high response rate 1,746 2.75% 48 7.60% 640

REDMOND DOWNTOWN

(42) med. response rate 835 5.70% 48 5.70% 660


Redmond Total 24,398 -- 500 -- 7,590

URBAN VILLAGE SEGMENT

(50) 10% 29,926 0.80% 238 7.95% 3,000

(51) 15% 32,972 1.03% 339 6.85% 4,950

(52) 20% 13,067 1.16% 151 5.78% 2,620

(53) 25% 8,801 1.08% 95 4.32% 2,210

(54) 30% 10,179 1.96% 199 6.52% 3,060

(55) 35% 11,738 1.67% 196 4.77% 4,110

(56) 50% 2,970 3.43% 102 6.87% 1,490

(57) 60% 3,220 2.86% 92 4.76% 1,940

(58) 75% 1,788 4.87% 87 6.49% 1,350

(59) 100% 2,760 5.11% 141 5.11% 2,743

Urban Village Total 117,421 -- 1,640 -- 27,473

TOTAL 1,495,642 -- 3,186 -- 54,213


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2.3 | SAMPLE MONITORING

Throughout the data collection period, RSG monitored response rates to ensure that the survey response

was on target, both overall and by individual segment. This monitoring included several steps.

1. RSG provided an internal project webpage—available to PSRC—that summarized demographic

distributions for households that completed the entire study and for household that only

completed Part 1 of the study. This webpage was updated automatically daily.

2. RSG also maintained an internal Excel workbook—available to PSRC—that forecasted final

completion rates based on observed response rate trends. These rates were monitored by

participation group (i.e., rSurvey™ vs. rMove™) to account for differences in methodology.

Updated forecasts were shared with PSRC on a biweekly basis.

3. RSG, PSRC, and the cities of Redmond and Seattle conducted a “midpoint review” after the first

few weeks of data collection to determine what adjustments might be needed to help meet

sampling objectives.

Overall response was monitored at several levels:

• Primary target: Meet the total number of households for the study (3,100 HHs across the region).

• Secondary target: Ensure that the response is proportional in each sample segment.

• Tertiary target: Ensure that the response is proportional across demographics or geographic

areas (e.g., by home county or region, UV, household size, income, and vehicle ownership).

During the data collection period, it was important to distinguish between various levels of monitoring

because it helped prioritize potential adjustments. For example, when the trends observed at the midpoint

review meeting indicated that the survey was below its target for households outside of King County,

RSG and PSRC decided to offer an extra incentive (an additional $10 per household) to the households

in those counties. Based on input from the City of Seattle, an extra incentive was also offered to select

Seattle UVs. The other adjustment made following the midpoint review meeting was to re-invite

households that had recruited but not completed Part 2 of the survey. These households were reassigned

to a new travel date for the last week of the study. Below is a full list of the potential adjustments RSG

discussed with PSRC and the cities of Redmond and Seattle:

Implemented:

• Reassign/re-invite households that missed their original travel dates.

• Offer larger incentives to households with low-response.

Not Implemented:

• Send additional reminder e-mails.

• Make additional recruitment and reminder phone calls.

• Keep the survey open longer.

• Conduct additional general outreach (e.g., press releases, media outreach).

• Conduct additional targeted outreach (e.g., fliers, door-to-door outreach).

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3.0 SURVEY DESIGN

3.1 | OVERVIEW

The 2017 study combined data collection methods, including smartphone, online, and telephone. The

goal of this design was to balance the strengths of innovative technologies with traditional experience and

best practices derived from traditional market research. This balances the need to adapt new survey

methods over time with the need to collect comparable results and conduct trend analysis. As described

in more detail in the following sections, the survey design included several stages to recruit and collect

data about households, their members, and their travel behaviors during the assigned travel period.

3.2 | SURVEY STAGES AND PARTICIPATION METHODS

As explained in Section 2.0, this study used a traditional ABS approach, and RSG notified invited

households via mail (see Section 4.0). The mailed materials instructed households to visit the study

website or call a toll-free number to complete Part 1 (the demographic “recruit” survey). Households

received instructions for Part 2 (the travel diary) upon completing Part 1.

PARTICIPATION GROUP ASSIGNMENTS

Part 1 of the study included two questions about smartphone ownership. Participants over age 18 were

asked to specify what type of smartphone they had (if any) and the phone’s age (i.e., is the phone less

than four years old?). RSG then used this information to determine group assignments. Group 2

participants were required to report their travel for one-day online using rSurvey, while Group 1

participants reported their travel for seven days using rMove. The goal at the start of the study was to

recruit approximately 20% of total households for Group 1. Based on typical rMove completion rates from

previous studies, only 140 households each week could opt into Group 1. The first 140 households in

which all adults reported owning rMove-compatible smartphones could opt into Group 1. The remaining

households were not offered this option and were assigned to Group 2.

TRAVEL DATE ASSIGNMENTS

All households were preassigned to a Tuesday, Wednesday, or Thursday travel date during the study

period. Travel days were assigned randomly but were proportional across days and within segments.

Households that opted into rMove participation were reassigned to a one-week travel period (always

beginning on a Tuesday) following the date on which they completed Part 1 (to allow time for the

household to download the app and prepare for data collection). rSurvey households that completed Part

1, but that did not complete Part 2, were eligible for travel date reassignment at the end of the study. A

total of 433 households were re-invited to participate on May 31, 2017. Of the re-invited households, 21

completed the study.

STUDY COMPONENTS

All households completed Part 1 either via the online survey or through the call center. (When households

called the call center, a representative utilized the identical online survey instrument, resulting in

consistent data coding for telephone and online responses.) Part 1 collected general demographic

information (e.g., household size, household income), established information to facilitate Part 2 (e.g.,


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home/school/work addresses, number of vehicles), and obtained any additional household-level

information (e.g., whether the home is owned or rented). Part 2 collected all trip and travel day

information and any person-level information (e.g., how often the participant bikes or uses transit). Group

1 participants reported their trip and travel day information through rMove and were asked to return to the

online survey to answer the additional person-level questions about typical travel behaviors and

preferences. (rSurvey participants answered these person-level questions as part of their travel diaries.)

FIGURE 2: STUDY COMPONENTS AND GROUP ASSIGNMENTS

LANGUAGE OPTIONS

The survey (as administered via rSurvey and rMove) was written entirely in English; the online surveys

included a built-in Google translate bar. Households that spoke Spanish, Russian, Chinese, Korean,

Tagalog, and Vietnamese could also call a separate toll-free line to complete the survey over the phone in

their preferred languages. Approximately 75 households used the online Google translate tool to

complete the survey.

3.3 | SURVEY INCENTIVES

RSG offered $10 gift card incentives—as advertised on the study mailed materials—to all households that

completed the study. Traditionally, transportation studies offer incentives to boost response rates and

decrease the overall cost of mailed invitations (i.e., without incentives, the number of required households

to invite increases. This increased mailing cost is greater than the cost of incentives). Invited households

could choose from physical or electronic gift cards from either Amazon.com or Starbucks. Households

also had the option to opt out of receiving a gift card. These were the same options offered in the 2014–

2015 (selected after testing in the 2014 pilot survey). While the study invitations advertised one $10 gift

card per complete household, some households were eligible to receive additional cards or increased

increments. All households that completed Part 2 using rMove earned $15 per adult. Households in

downtown Redmond qualified for an additional $10 at the household level (part of the Redmond outreach

plan).

15

When RSG and PSRC noted that the response rate outside of King County was below target (during the

midpoint review), the incentive plan was adjusted further—all households that recruited into the study

after the adjustment and lived outside of King County qualified for an additional $10 at the household

level. RSG made the same adjustment for households in selected UVs10 that were below target and

prioritized by the City of Seattle during the midpoint review. These adjustments were designed to increase

completion rates among key demographics and geographies. The direct impact of the additional

incentives was inconclusive, because the final number of households offered the extra incentive was

relatively small and because response rates for the entire region decreased slightly over the course of the

study period.

3.4 | HOUSEHOLD, PERSON, AND VEHICLE DATA COLLECTED

Part 1 of the survey was the main collection source of household, person, and vehicle data. Households

could complete this section any time after the study opened, and up to eight days after their assigned

travel dates (when their household travel diaries closed). Part 1 was organized into the following question

categories:

1. Vehicle ownership.

2. Household membership details (e.g., age, relationship, smartphone ownership).

3. Work and school information.

4. Home and previous home details.

5. Home location preferences.

6. Household income.

7. Incentive and communication preferences.

8. Part 2 completion instructions.

The survey collected all address information for current and previous “habitual” locations (e.g., home

address, work address, school address) using a built-in real-time geocoder (see Figure 3).

10 Belltown, Greater Duwamish, Northgate, Pike/Pine, Pioneer Square, South Lake Union, Upper Queen Anne, and Uptown


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FIGURE 3: PRIMARY WORKPLACE LOCATION GEOCODER (RECRUIT SURVEY SCREENSHOT)

3.5 | TRAVEL DIARY DATA COLLECTED

TRIP DATA

Although the rMove and rSurvey platforms varied slightly in user interface/design, most of the information

collected was the same. Figure 4 and Figure 5 show example trip rosters in each platform.

Both rMove and rSurvey gathered the following information from participants:

• Data obtained as explicit questions for both modes (rSurvey and rMove)

− Travel party.

− Trip purpose.

− Trip mode(s).

− Trip costs and other details associated with each mode (e.g., access/egress modes,

parking details).

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• Data obtained automatically and passively by rMove and asked as explicit questions in rSurvey

− Trip start and end points

− Trip start and end times

− Trip roster

Although much of the collected travel data was the same across platforms, the collection method was not.

Group 2 (rSurvey) participants reported all their trip information through recollection whereas rMove

collected trip location and time details passively for Group 1 (rMove) participants. (Trip details that could

not be passively recorded – such as travel party, trip purpose and mode – were recalled for both groups.)

In practice, this often meant that trip start, and end times were more specific among Group 1 trip diaries

because rMove collected exact times, whereas the Group 2 diary only recorded times in 5-minute

increments. Moreover, when participants are asked to recall all the details their trips, they frequently

round departure and arrival times to the nearest 15 minutes (resulting in less precise reports). Group 1

participants could correct passively collected trip data in rMove by splitting their trips into multiple

segments, merging their trips, or adding entire trips. They could also report rMove errors (e.g.,

erroneous/spurious trips). About 4.2% of trips were edited by rMove participants, and about 2.7% of trip

surveys reported errors. Group 1 (rMove) participants were still asked to recall their trip purposes and

travel parties, among other details.

FIGURE 4: rSURVEY TRIP ROSTER (SCREENSHOT)


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FIGURE 5: rMOVE TRIP ROSTER (SCREENSHOT)

TRAVEL DAY DATA

In addition to all trip data, the surveys collected day-level information at the end of each travel day (one

day for rSurvey participants and seven days for rMove participants). In both cases, this information

included the following:

• Whether that day was typical or atypical (rSurvey one-day diary only).

• Why the participant made no trips (when that was the case) that day.

• What types of deliveries occurred that day.

• How much time the participant spent telecommuting or shopping online that day.

CHILD REPORTING

Although Part 1 collected information on all household members, Part 2 did not require the same level of

participation for both children and adults. Adults participating through rSurvey were required to complete

full travel diaries for all children between the ages of 5 and 18. During postprocessing, RSG derived

individual trip records for children under the age of five based on the trips on which they traveled

(reported in the travel party on trips made by other household members). rMove asked Group 1 adults to

provide trip information for children under age 18 when no household adult was on the trip (e.g., bus to

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school), but were not required to answer any day-level information for their children. In both rMove and

rSurvey, adults were still asked to report children of any age when they were present within their travel

parties.

3.6 | ADDITIONAL DATA COLLECTED

The survey questionnaire also included questions about general travel behavior and preferences that all

participants were required to complete to receive an incentive.11 These questions were only available

online, so rSurvey participants answered them as part of their travel diaries while rMove participants

returned to the online platform following their travel diary completion. These questions included the

following:

• Various mode frequencies (how often does the participant typically walk, bike, or use transit, ride-

sharing, or car-sharing systems).

• (If uses transit) availability and use of various transit fare payment methods (e.g., cash/tickets/Flex

Pass)

• (If travels to school and has a transit pass) school’s contribution to transit pass costs.

• Employer transit subsidies and commuter benefits.

• Autonomous vehicle concerns and interests.

• Factors that would encourage increased bike/transit usage.

Various questions were skipped based on age or reporting method. For example, if a participant was

under age 16, then they were not asked about their use of car-sharing systems. Also, if a participant’s

survey was reported by proxy (someone else was answering for them), then they were not asked opinion

and preference questions.

3.7 | SURVEY DESIGN UPDATES

While most of the survey design remained consistent with the 2014–2015 study, there were several

additions and changes. RSG and the study sponsors implemented these changes—listed in the sections

below—to accommodate a combination of regional behavior/transportation shifts and new developments

in survey research.

STRUCTURAL CHANGES

In 2015, RSG and the study sponsors invited a panel of participants from the 2014 study to complete a

small-scale rMove study. The rMove data were not combined with the 2014 main study data. In contrast,

the 2017 study included both rMove and non-rMove (rSurvey) participants within the same data collection

period, and RSG provided combined data. The data combination process is described in Section 5.0. The

2017 study included many of the same person-level questions as the 2014 study. However, RSG

rearranged the question order to capture key details about typical travel behavior in Part 1 (e.g., school

and work commute details) rather than later in the study. The sections below list all content changes in

the 2017 study.

11 All rSurvey participants included in the final dataset answered these questions. rMove participants were required to answer these questions to receive an incentive, but were not excluded from the final dataset without them.


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CONTENT CHANGES: ADDED/MODIFIED QUESTIONS

• Race/ethnicity: The study sponsors added this question in 2017 to inform equity analysis. The

question’s style closely matched the ACS race/ethnicity format. (See Part 1 questionnaire, pg. 20.)

• Commute details: These questions were streamlined to reduce respondent burden (e.g.,

workplace type included only one question rather than multiple questions). (See Part 1

questionnaire, pg. 24.)

• Bike/transit use factors: These questions change slightly each year based on PSRC planning

needs. (See Part 2 questionnaire, pg. 41-42.)

• Smartphone details: The 2017 study asked only generic (rather than detailed) compatibility

questions to determine whether each household was eligible to participate in Group 2. (See Part 1


• Toll frequency: This question was revised to include a day-level filter (i.e., ask if the participant

used a toll road on travel day, rather than ask about toll roads for each trip). (See Part 2


• Transit pass availability: The 2017 study included more answer options (i.e., use/no use,

availability, and nonapplicable). (See Part 2 questionnaire, pg. 36.)

• Home delivery: This question’s format varied slightly across Group 1 and Group 2 surveys. Group

1 participants were asked to indicate which types of deliveries occurred on each travel day while

Group 2 participants were asked to indicate how many of each type of delivery occurred on the

travel day. (See Part 2 questionnaire, pg. 33.)

• Travel-replacement activities: The 2017 study included two questions that asked participants to

provide the time they spent teleworking and online shopping on their travel days. (See Part 2


There 2017 study also included several logistical updates to ensure that the survey was as current as

possible. (For example, RSG updated the list of vehicles from which participants could select their

household vehicles’ year/make/model.)

CONTENT CHANGES: DROPPED QUESTIONS

The following questions were dropped in 2017 either to reduce response burden or to keep the survey up-

to-date:

• Travel info use (e.g., websites, apps used for real-time travel info).

• Parking maps (e.g., work parking location, trip parking location).

• Bikeshare questions/answer options.12

• Willing to participate in future studies (data will be destroyed at the end of the study for privacy

reasons).

12 The 2017 data collection effort took place after the Pronto bikeshare program was discontinued and before the new dockless bikeshare systems were introduced in summer 2017, therefore questions about bikeshare use during this time could not be asked.

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4.0 SURVEY BRANDING, COMMUNICATION, AND ADMINISTRATION

4.1 | STUDY BRANDING

RSG developed the study branding collaboratively with PSRC, reusing many design aspects from the

2014–2015 study. The complete branding package included the study name, logo, color scheme, and font

selections. The final 2017 study logo is shown in Figure 6.

FIGURE 6: 2017 STUDY LOGO

4.2 | STUDY INVITATION MATERIALS

Each invited household received three mailings:

• Prenotice Postcard: RSG sent prenotice postcards to invited households in nine waves—each

wave corresponded to a preassigned travel week. These postcards (arriving approximately 1.5

weeks before the household’s assigned travel date) notified households that a formal study

invitation would be arriving shortly, and that they would be offered an incentive upon study

completion. The postcards also invited households to log on to the website or call the toll-free

number to learn more about the study and to complete the first portion of the study.

• Invitation packet: Formal study invitation packets arrived at each household approximately three

to four days before the assigned travel date. The cover letter explained the study purpose,

described the steps necessary to complete the study, and included the study sponsors’ logos and

a signature from PSRC’s executive director, Josh Brown. The invitation packet also included an

Frequently Asked Questions (FAQ) sheet.

• Reminder Postcard: Reminder postcards arrived at each household approximately two or three

days after the invitation packet to encourage every household to complete the study. Like the

initial postcards, these cards included the study phone number, website address, and participant

login information.

All mailings were written in English, but the postcards and letter also included separate phone numbers

for non-English-speaking participants. The additional languages offered on the postcards were Spanish,

Chinese, Korean, Russian, Tagalog, and Vietnamese. PSRC coordinated these language offerings

except for Spanish, which the study call center coordinated. An example postcard is shown below in

Figure 7 (front) and Figure 8 (back), and examples of all printed materials can be found in the Appendix.


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FIGURE 7: EXAMPLE SURVEY POSTCARD (FRONT)

FIGURE 8: EXAMPLE SURVEY POSTCARD (BACK)

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Changes to the printed materials in 2017 included omitting the printed travel log (memory jogger insert)

from the invitation packet and discontinuing the second reminder postcard. These materials were less

critical to study success and provided an opportunity to reduce printing and postage expenses.

Undeliverable mail was sent to a UPS box in Seattle that RSG reserved specifically for this study. Each

week, UPS counted and recycled the returned mail. 2,704 letters were returned throughout the study,

which comprised approximately 5% of all study invitations. The percentage of returned mail varied by zip

code, from about 3.5% - 7.5%, but there were no significant patterns to explain different rates. (For

example, areas with higher resident turnover did not necessarily exhibit higher rates of return mail.) The

overall return mail rate during the 2014 study was just over 10%, so the 2017 return rates were

significantly lower than those in this region two years ago. The 2017 return mail rates were also

comparable to similar projects in other regions of the United States.

4.3 | STUDY WEBSITE

RSG developed a project website in 2014 to describe the 2014–2015 study and facilitate survey

participation. This site was maintained in the interim, and the site design was updated in 2017 to reflect

the new study and to provide more current information (e.g., updated FAQs, quotes of support, sponsors).

The 2017 website (like the 2014 website) was designed to be simple, intuitive, and easy to navigate on

desktop computers and mobile devices. When the study was halfway complete, RSG and PSRC agreed

on several study-related “fun facts” that RSG then added to the study website to increase engagement

and encourage further participation. These facts were selected to generate interest while avoiding

potential unintended bias in future survey responses. The selected facts RSG added to the website

included the following:

1. 52% of participants rate “being close to family or friends” as somewhat or very important in

choosing their current home locations.

2. 18% of employed participants telecommute at least one day per week.

3. The top three previous home states among participants moved to Washington in the past five

years are California, Michigan, and Massachusetts.

4. 24% of participants have lived in their current homes for 10+ years.

While the study was collecting responses, participants could click on a button at the top of the study

website that took them directly to the survey’s password entry page. Once the study closed, this button

was replaced with a brief message about the study’s closure; this message is shown in Figure 9.


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FIGURE 9: PROJECT WEBSITE HOME PAGE

4.4 | PUBLIC OUTREACH

TARGET AUDIENCES

The primary goal of all outreach activities in a randomly sampled household travel study is to maximize

participation from invited households using outreach messaging and methods to accomplish the following:

• Communicate the value and importance of the study for local and regional planning needs.

• Legitimize the study and engender trust that collected data would be used appropriately.

• Develop a positive and consistent presence.

• Increase invited households’ understanding, awareness, and acceptance of the study.

• Reinforce study invitation messaging and clearly define lines of communication.

Outreach activities were not limited to invited households, but the goal of all activities was to increase

study response among those invited (i.e., outreach activities were not designed to recruit volunteers).

OUTREACH ACTIONS

RSG implemented the following actions to promote the study:

• Updated the official project website: RSG worked with PSRC, the City of Redmond, and the

City of Seattle to update the survey website used during the 2014–2015 study. These updates

included changes to the website text, adding news articles to the “News” section, and updating the

study sponsors. These steps reinforced the continued relevance of the 2017 study.

• Developed a formal press release: RSG worked with PSRC, the City of Redmond, and the City

of Seattle to finalize language for a formal press release that the sponsor agencies could post on

their respective websites and distribute to local media.

25

• Ran Facebook advertisements: The study team allocated a small expense budget to create and

run a Facebook ad in a few specific geographies throughout the study region. This was conducted

as a small test; the cost-effectiveness of this kind of outreach was unknown at the start of the

survey (i.e., whether social media ads could be targeted to raise awareness among invited

households rather than the general population). Additional details are listed in the following

section.

Additional outreach activities that were recommended for consideration by the study sponsors included

cross-posting the press release or other study information on the websites of other study partners (e.g.,

local jurisdictions within the study area); coordinating with community groups and organizations to

increase support and awareness among local communities; and posting updates on official social media

channels. While not all outreach activities were enacted in the 2017 study, PSRC may re-evaluate these

and other options for future study waves, based on updated study priorities.

FACEBOOK ADVERTISEMENT

The Facebook advertisement sought to increase awareness among invited households. While Facebook

can serve as a great tool for targeting specific demographics, it can be more challenging to target

individual households in a random sample. To increase the cost-effectiveness of running the ad, RSG

collaborated with the study sponsors to select several small geographies with high densities of invited

households. The smallest target geography Facebook offers is a one-mile radius around any provided

address. RSG conducted analysis to determine the optimum mile-radius areas and solicited input from

the cities of Redmond and Seattle, ultimately choosing the following locations:

• 16345 Cleveland Street, Redmond

• 7742 E Marginal Way S, Seattle

• 1208 E Jefferson Street, Seattle

• 4505 17th Avenue NE, Seattle

These four locations were selected to support priorities from the cities of Redmond and Seattle (i.e.,

prioritizing downtown Redmond and certain UVs in Seattle); these also represented areas with high

invitation densities. RSG specified in the ad setup that only users with home addresses within the radii

should be targeted.13 The goal of this setting was to limit ad costs by excluding visitors to the area.

The final ad settings were as follows:

• The ad was set to optimize clicks (i.e., send traffic to the study website).

• Facebook used automatic bidding and budget management to manage the $2,600 allotted to the

advertisement.

• The ad was shared from a study-specific page14 that contained introductory study information.

• When clicked, the ad sent users to the homepage of the study website.

• The ad ran throughout the recruitment period—from March 20, 2017 to May 24, 2017.

The final ad design is shown in Figure 10.

13 Facebook uses several data sources to determine home location, including reported home city, device location services, and other device connection information. 14 Facebook page developed for the 2017 study (no longer active): https://www.facebook.com/2017PSRTS/).

https://www.facebook.com/2017PSRTS/


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FIGURE 10: FACEBOOK ADVERTISEMENT

The goal of the 2017 study’s Facebook ad was to increase survey awareness/legitimacy, so the ad

analytics did not track its direct impact on survey response. However, the ad acted as a trial run if PSRC

chooses to use Facebook ads as a response stimulant in future waves.15

Facebook Ads Can Effectively Reach Large Audiences at Low Costs

The ad reached 116,338 people during its two-month run period – 8,108 of whom clicked on the ad.

Facebook Ads May Reach a Different Audience Than Typically Reached Using Mailed Invitations

The most common group (and cheapest, as measured by cost per unique click) that responded to the ad

was between the ages of 18 and 24. This has traditionally been a hard-to-reach population through

mailed invitations. Compared to the average of $0.32 per unique click, this age group cost $0.28 per

15 The following list includes basic Facebook analytics definitions:

- Impressions: Count of unique views (includes multiple views from the same people). - Reach: Count of unique viewers. - Frequency: Average number of times each person saw the ad. - Unique link clicks: Count of unique viewers who clicked the link. - Cost per unique click: Average amount spent to achieve one unique link click.

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unique click. The 25–34-year-old age group was also below average in cost at $0.28 per unique click

(Table 4). While the age composition in a Facebook sample may be different than in a mailed sample,

there are likely many other characteristics that would vary across sample types. If the behavioral

composition of Facebook-recruited households is different than the composition of mail-recruited

households, it is also possible (perhaps even likely) that these households/individuals would also respond

to surveys differently on a general level (e.g., require different incentives, expect different outcomes, etc.).

Broad Audience Criteria Does Not Necessarily Guarantee a More Diverse Sample

Although the individuals who viewed the PSRC ad were diverse in many respects, the ad was originally

optimized to increase “clicks,” which also meant that Facebook targeted certain types of individuals more

heavily, without specifying these targets at the outset. For example, over 90% of the people who viewed

the ad viewed it on a mobile device (Table 5). This is partially because far more Facebook sessions occur

on mobile devices than on desktops. Furthermore, the mobile device reach was split evenly between iOS

and Android devices, but significantly more Android users clicked on the ad. It may not be entirely

surprising that iOS and Android have different responses to Facebook ads (e.g., dissimilar click,

purchase, and app-install behaviors), but these differences may warrant closer monitoring and

consideration when Facebook is used for sampling to avoid possible biases.

Facebook Analytics Provides Limited Geographic Analysis

For privacy reasons, Facebook limits the level of analysis to broad location categories. This is not a

concern if the goal is to drive survey responses (and the survey asks participants to provide a home

address), but it may affect other early-stage reporting.

Ads Are Easy to Create and Adjust Mid-Campaign

Facebook provides extensive resources to set up and monitor advertisements. Ads are flexible and can

be easily adjusted after launch. Whereas mailed invitations take at least two weeks to fully implement and

realize, Facebook ads can change in minutes.

TABLE 4: PERFORMANCE BY AGE

AGE IMPRESSIONS REACH UNIQUE LINK CLICKS

COST PER UNIQUE CLICK

CONVERSION (REACH CLICK)

18–24 162,797 28,069 1,816 $0.28 6.47%

25–34 147,060 33,089 1,896 $0.28 5.73%

35–44 91,230 20,780 1,220 $0.32 5.87%

45–54 72,585 14,259 1,172 $0.34 8.22%

55–64 67,386 11,157 1,156 $0.36 10.36%

65+ 55,757 8,984 848 $0.41 9.44%

TOTAL 596,815 116,338 8,108 $0.32 6.97%


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TABLE 5: PERFORMANCE BY DEVICE

DEVICE IMPRESSIONS REACH UNIQUE LINK

CLICKS

COST PER UNIQUE CLICK

CONVERSION (REACH CLICK)

Android Smartphone

379,794 57,355 6,912 $0.31 12.05%

iPhone 192,520 53,379 920 $0.40 1.72%

Android Tablet 10,773 2,317 236 $0.27 10.19%

iPad 10,690 2,348 36 $0.56 1.53%

Desktop 1,653 720 -- -- 0%

iPod or Other 1,385 219 6 $1.65 2.74%

TOTAL 596,815 116,338 8,108 $0.32 6.97%

City of Redmond Outreach

The City of Redmond conducted additional activities in their region to maximize participation from their

sample, particularly in the downtown Redmond sample area. The city’s primary activities included the

following:

• Sending an additional letter to households invited to the study to further reinforce the fact that local

agencies supported the effort (not just the regional agency).

− Initially, this additional letter only went to households in the downtown sample area; after

the first few weeks, the additional letter was sent to all invited households.

• Offering an additional $10 incentive to downtown households (as previously discussed).

• Deploying a Facebook ad across the city (like in Figure 10). The specific details of the cost of the

ad are available through the City of Redmond.

4.5 | PARTICIPANT SUPPORT

OUTBOUND PARTICIPANT SUPPORT

RSG used several types of outbound participant support throughout the study in addition to the website

and printed invitation materials discussed previously. The primary sources of outbound support were

automated e-mail reminders, reminder phone calls, and in-app reminders or notifications (rMove

participants only).

E-mail Reminders and Phone Calls

During Part 1 of the survey, participants selected how they preferred to receive reminders. RSG required

all rMove participants to provide e-mail addresses, and an additional phone number was optional. rSurvey

participants could provide just one or the other (phone or e-mail), but most households provided both.

Any household that provided an e-mail address received e-mail reminders, while households that only

provided a phone number were reminded by phone.

The study call center conducted all phone reminders. These reminders occurred on the following

schedule:

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• One day before each household’s travel date.

• One day after each household’s travel date.

• Three to five days after each household’s travel date (if the household had not yet completed the

study).

As time and budget allowed, the call center also called households who had not yet recruited when a

phone number was matched to the invitation address. These calls legitimized the invitation letters and

encouraged recruitment.

Reminder e-mails occurred on a similar schedule, although more frequently. RSG sent e-mail reminders

on the following schedule:

• One day after each household completed Part 1.

• The Friday before each rMove household’s travel week (this reminder also included instructions

for how to download rMove).

• One day before each household’s travel date.

• One day after each household’s travel date (Group 2 only).

• Three to five days after each household’s travel date (if the household had not yet completed the

study).

rMove households received 1–2 additional e-mails one week after the end of their travel weeks. These e-

mails reminded them to return to the online survey to complete the preference questions.

In-App Reminders (rMove)

rMove participants also had in-app reminders to encourage them to complete all surveys during the entire

travel period. Participants received notifications as soon as a new survey was available—either several

minutes after the end of a trip or the morning after a full travel day. rMove participants reporting for their

children by proxy also received reminders to review and add to their children’s trip rosters, if needed.

Participants had the option to turn off reminders or GPS tracking, but RSG instructions encouraged them

to leave these features enabled to ensure that they did not miss any portion of the study. As with most

apps, it is not currently possibly to know if or when participants turn off their GPS tracking.

INBOUND PARTICIPANT SUPPORT

In addition to all outbound participant support, RSG provided three primary means through which

participants could contact study administrators. All participants could call a toll-free number to reach the

study call center or e-mail the study inbox with questions. rMove participants also had the option to

submit feedback directly through the app. The study website included the toll-free number, study e-mail,

and contact information for representatives from PSRC. Anyone with a question or comment could

contact the study team or could contact PSRC directly for information, whether they were a participant or

simply an interested member of the public.

Participants who called the toll-free number were either connected to a trained representative who could

walk them through any issues or answer any questions, or they were asked to leave a voicemail. In total,

the call center received 322 inbound calls and made 1,503 outbound calls (primarily reminder calls). RSG

staff monitored and responded to the study e-mail inbox and rMove feedback. Inbound communications

typically received a response within one business day. Table 6 below shows the breakout of inbound e-


30

mails and rMove feedback messages, by topic. (Each e-mail is included in only one category, although

some e-mails entailed more than one topic.)

TABLE 6: INBOUND E-MAIL TOPICS

E-MAIL TOPIC COUNT

Gift card inquiry 195

General questions 195

rMove troubleshooting 173

No reply needed (e.g., “Thank you”) 107

Comments and feedback 98

Completion status 87

rSurvey troubleshooting 82

Forgot password 81

Volunteer inquiries 71

Travel date reassignment request 53

Unsubscribe request 41

31

5.0 DATASET PREPARATION

5.1 | OVERVIEW

Dataset preparation and quality control procedures exist at every stage of a study. Before fielding a study,

data collection instrument development and testing confirms that survey responses are recorded

correctly. During fieldwork, the data collection instruments employ numerous real-time validations and

logic checks to ensure consistent coding and logical response combinations and to prevent skipped

questions. After the data collection period ends, additional time is spent reviewing, cleaning, and

processing the raw data to prepare the unweighted dataset for analysis. This includes combining the

travel data collected with the different survey instruments (rMove and rSurvey). The following sections

summarize these stages of dataset preparation and quality controls undertaken for the 2017 study. A

separate Dataset Guide was provided with the initial dataset and includes more specific details for key

elements.

5.2 | DATASET PREPARATION

DATABASE SETUP AND REAL-TIME QUALITY CONTROLS

Prior to a survey launch, RSG conducts quality control on the survey instruments. For the recent study,

this included reviewing map displays, question wording, and graphical elements to ensure that the survey

and app interfaces were clear and easy to use and that questions were understandable. The goal was to

make participation as easy and as intuitive as possible. The prelaunch review process also included both

manual and automated review of survey functionality (confirming logical flows and validation) and

database records (confirming survey responses and accuracy of passively recorded data).

Real-time data checks employed during data collection help minimize respondent burden and increase

response completeness and consistency. This helps reduce data cleaning and recoding after the survey

is complete. Examples of these checks include the following:

• Validation logic preventing skipped questions (requiring an answer to each question before

moving to the next).

• Logic checking to hide/skip questions or answer choices that are not relevant (e.g., not asking

employment questions for children).

• Spatial and temporal checks within each person’s trip roster (preventing overlapping trips).

• A “copy trips” feature in rSurvey to reduce respondent burden and ensure consistency of

locations, modes, and trip times for jointly made household trips.

These real-time data checks do not eliminate every inconsistency, but they do significantly reduce

reporting errors and the need for recoding after data collection is complete.

rMove also included tools to allow participants to validate or correct passively recorded trips during data

collection. Participants could split trips (if a short stop, like dropping someone off, was missed), merge

trips (if a false stop, like sitting in traffic, was detected), or flag trips if they appeared to have other types of

errors (e.g., imprecise routes or times). rMove participants could also add or drop trips entirely throughout

their travel period. These user-edits were flagged in the database to be reviewed further by analysts after

data collection was complete.


32

GEOGRAPHIC DATA CHECKS

Geographic data collected in the online survey underwent several review and processing steps during

and after data collection. (rMove GPS data are reviewed and processed separately as described in the

next section.) During data collection, rSurvey used the Google Maps API to geocode the coordinates for

reported home, work, school, and trip addresses. The API was also used to estimate travel times and

distances. (These estimates were recorded in the database and shown to participants in real-time to help

them verify that they had entered their trip location information correctly.)

Following data collection, RSG took additional steps to process geographic data from rSurvey:

• Reviewed trip distances and speed (by mode) for extreme values to identify potential issues.

• Coded home location points to BGs and broader regional definitions.

• Reviewed differences between sample (invited) home addresses and reported home addresses.

Home Location Review and Selection

Like the 2014 study, the home locations reported in the recruitment survey for the 2017 study were

compared to the sampled home locations (where invitations were mailed). For most participants, these

locations were identical, but some participants reported a different address in the survey. Often the self-

reported address is likely correct (e.g., indicating that someone had recently moved, and the survey

invitation was forwarded to them); however, occasionally the self-reported address was for a

nonresidential location or was reported at a less precise geographic scale than desired. These

differences may be due to someone attempting to protect their privacy (e.g., reporting a nearby

intersection or business rather than their home) or may be due to someone simply reporting their general

home location (e.g., only reporting the street, city, or Zip Code). Home addresses that were not precisely

reported were reviewed more carefully and compared to the sample address and the trip records to

determine which home address is most accurate. The recommended hierarchy for reviewing and

selecting the final home address for use was as follows:

1. If a precise street address was reported, use the reported address.

a. These may be compared to the sampled home address, but unless a business or other

nonresidential address is clearly reported, RSG generally assumed that the reported

address was current and correct.

2. If an imprecise address was reported (e.g., intersection or street without an address, or just a city

or Zip Code), compare to the sample address and trip data to determine which address was most

accurate:

a. If the participant used rMove, review the GPS locations for any trips where the

destination purpose was “home” to evaluate the reported home location.

b. If the participant used rSurvey, review the destination for any trips where the destination

place name or purpose was “home” to evaluate the reported home location.

c. If a more precise and accurate home location is not available in the trip data, review the

sample address in relation to the reported address to determine if the sample address

was likely current and accurate.

33

GPS DATA REVIEW AND QUALITY CONTROLS

Before RSG combined the rMove and rSurvey datasets, the rMove data underwent a multistep cleaning

process to reduce the number of false/spurious trips the device collects and identify short stops that

rMove may have missed (e.g., dropping someone off). Additionally, many participants are not consistent

with how they edit trips, thus requiring cleaning after participants send data from their devices at the end

of the study.

rMove data cleaning and processing occurred in three stages:

1. Automated data cleaning: A machine learning algorithm automatically classified trips (based on

previous, manually reviewed datasets) to identify which trips should be automatically dropped,

which could be kept as is (without additional review), and which trips were likely to need

additional review by analysts in the next stage.

2. Manual spatial review and correction: Analysts reviewed trips and trip-path data to determine if

one of three possible “corrections” were needed:

− Dropping/removing a trip from the dataset (e.g., a participant walking around his yard is

not a valid trip).

− Splitting a trip where an additional stop was apparent (e.g., a participant stops at a

coffee drive-thru). In these cases, the answers from the initial trip were applied to all

resulting trips after the split.

○ Joining a trip where a stop between two trips was not apparent (e.g., mobile

device lost signal temporarily in a tunnel or the participant was stopped in traffic). In

these cases, the analyst chose which trip’s survey answers were applied to the

resulting joined trip. Often, the original answers were the same for both surveys.

There were some cases in which a gap between two trips was apparent, but the two

trips were not joined because the missing trip path was unclear. These cases

generally occurred when a user added a trip that did not fit into the details of the

bookend trips. These records were flagged as “teleport” trips in the final dataset.

3. Scripted processing and derivations: The final stage included various scripted trip corrections

and derivations on the initial cleaned dataset. Example steps include the following:

− Removing points with low accuracy (e.g., ranges greater than 250 meters) and re-derived

trip-path distance.

− “Trimming” departure and arrival times at the beginning or end of trips (where spurious

points were collected) to ensure accurate trip times.

DERIVATION OF TRIPS FOR NONPARTICIPATING HOUSEHOLD MEMBERS

Household travel studies require data for all household members to assess complete household travel

patterns. However, in the data collection process, some exceptions are allowed where travel can be

reported by proxy, particularly for children. In this study, there were three main ways that travel data could

be reported or derived for nonparticipating members:


34

• In rSurvey, a travel diary was not collected for children under the age of five. However, these

children could be reported as travel party members on trips made by other household members.

Whenever this occurred, that trip was copied to the child’s record.

• In rMove, only adults age 18 or older were required to use the app on their smartphones. As with

the youngest children in rSurvey, if an rMove household child was reported as a travel party

member on an adult’s trips, then those trips were copied to the child’s record.

• In rMove, if a household had children, one adult was designated to proxy report trips within the

app for children. This adult was asked to add trips to a child’s roster if the child made an

independent trip (e.g., riding the bus to school) or made a trip with someone outside of the

household (e.g., getting a ride with a friend’s parents).

For children under the age of five, a diary was not required to reduce respondent burden; it is assumed

that children in this age range rarely make independent trips. For rMove households, children were not

asked to use the app both because of potential privacy concerns and because children may not have

their own smartphones or may be less likely to use the study app consistently.

INCOME IMPUTATION

Households had the option of reporting income in ten categories or selecting “prefer not to answer”.

Those that selected “prefer not to answer” were asked a follow up income question with only five, broader

income categories. Then, household income was imputed for the 7% (234) of households that preferred

not to report income, rather than forcing those households to provide an answer. The imputation was

done using a logistic or “probit regression” model of income category, for the households that did answer

the detailed income question. The model includes attributes of the household (worker status, education

level, head of household age, home ownership, and housing type) as well as the income distribution in

the residence block group, based on the 2008-2012 5-year ACS. The model was estimated using R using

the “polr” function.

The household income variable imputation used for weighting was created using the following rules:

• If the household answered the detailed income question, the reported detailed category is used.

• If the household did not answer the detailed income question but did answer the broad income

follow up question, the broad income answer was used.

• The provided income for the 3,057 households was used for model estimation (2,923 providing

detailed income, and 128 providing broad income category).

• If the household neither answered the detailed nor the broad category income question, a probit

regression model was run in R to impute a household income into the broad income category to

match the Public Use Microdata Sample (PUMS) targets categories (234 households imputed).

INTEGRATION OF DATA FROM MULTIPLE RETRIEVAL MODES

After the rMove data were cleaned, the rMove and rSurvey datasets were combined, reviewed, and

cleaned again. This process involved the following steps:

1. Merged rMove and rSurvey variables: In most cases, the daily and trip survey questions were

identical or similar. However, in a few cases, the questions were formatted somewhat differently

and resulted in slightly different variable types. Wherever feasible, RSG reconciled differences

35

between variables (rather than kept them separate). In most cases, variables were recoded to

retain as much detail as possible. These recoded variables included:

o Trip mode and purpose

o Trip parking details

o Trip toll details

o Trip cost/payment details

2. Aligned the rMove and rSurvey travel days: Traditionally, travel diaries collect data for a single

24-hour period—from 3:00 a.m. to 2:59 a.m. on the following day. This shift is used to account for

trips that extend beyond (or take place after) midnight (e.g., shift workers or people returning from

an evening out) but are still part of the “day” that ends at home. The rMove app collects travel

diary data for multiple days and—for many reasons—currently defines midnight (calendar day) as

the division between days.

To combine the rMove and rSurvey data into one, consistent dataset—and to retain the

“traditional” travel day definition used in most travel models—the rMove “days” were redefined

with 3:00 a.m. breakpoints. This resulted in the following outcomes:

− Trips that were recorded between midnight and 3:00 a.m. on a given day were assigned to

the previous day trip totals/counts (e.g., trips between midnight and 3:00 a.m. on Saturday

morning were flagged and counted as part of the “Friday” travel day).

− No trips were recorded between midnight and 3:00 a.m. after the LAST travel day

(Monday), so this day does not cover a complete 24-hour period.

− Trips recorded between midnight and 3:00 a.m. on the FIRST travel (Tuesday) were not

part of the newly defined travel period. However, these trips were used as a “proxy” for the

midnight to 3:00 a.m. period after the LAST travel day (see above). Trips that took place

from midnight to 2:59 a.m. on Tuesday morning were listed as “day number = 7,” though the

travel date variable still reflected the correct date. Fewer than 1% of all trips required this

shift.

Some data elements in the combined dataset required derivation or slight adjustments due to differences

between retrieval mode. For example, rSurvey obtained details that were not explicitly asked in rMove

(e.g., transit routes and park-and-ride lots used, nicknames, and street addresses for trip destinations).

Similarly, the rMove instrument obtained some details that were not asked in rSurvey (e.g., specific transit

and parking costs). Wherever possible, rMove and rSurvey responses were combined into consistent

variables, but where more detail could be retained (such as cost details), separate variables were

maintained.

COMPLETION AND EXCLUSION CRITERIA

Completion Criteria

The last step of dataset preparation involved review of all data records to confirm that they met survey,

travel day, and household completion criteria. All households were considered complete if they met the

following conditions:


36

1. The household completed the recruit survey by answering all required questions.

2. All household members completed all trip and travel day surveys on at least one concurrent day

throughout their travel period.

All rSurvey households have a single complete travel day. rMove households must have at least one

complete travel day (where all surveys are completed on the same day by all household members), but

may have up to seven completed travel days. Partially complete rMove travel days are also included and

indicated by a variable in the dataset for households that have at least one fully complete day. Among the

households that completed at least one travel day, all but six households completed at least one

weekday.

Exclusion Criteria

Households were excluded for several reasons. The primary reason for exclusion was that all household

members did not complete all trip and daily surveys on at least one concurrent day. Households that

indicated a new home address outside the study region were also excluded from the final dataset.

Additional exclusion criteria may be included after PSRC reviews the first draft of the dataset.

37

6.0 EXPANSION AND WEIGHTING

6.1 | ROLE OF WEIGHTING

Household travel surveys cover a fraction of the population, yet the resulting datasets help analyze and

make inferences about the population at large. Weighting is the process of comparing selected

demographics in the survey to external control data such as the Census or the ACS and adjusting the

profile of the survey dataset to improve the representativeness of the population in the study area.

Survey data weighting for this project involved three primary steps:

1. Calculating initial expansion weights to expand the sample to represent the study area

population.

2. Adjusting the initial weights to meet marginal population distributions of key household and

person-level sociodemographic measures.

3. Calculating trip (and travel day) adjustment factors to account for known reporting biases

associated with certain (or any) data collection methods.

6.2 | STEP 1. EXPANSION BASED ON SAMPLING PROBABILITIES

Weights were developed for the PSRC 2017 dataset. First, the number of survey households was

expanded to the number of households in each sampling strata by assigning an expansion factor to each

household based on the sampling rate (see Table 7). All residential addresses within each sampling

strata (described in the study sampling plan) had an equal probability of being invited to the study, but

invitation rates varied between the strata to account for targeted oversampling and to account for

“compensatory oversampling” where response rates were expected to be low. Each stratum includes

separately calculated expansion weights to account for the differences between the probabilities of being

invited in each of the various strata. Dividing the number of households present within the stratum (using

the most recent Washington State Office of Financial Management (OFM) April 1, 2017 official population

estimates16) by the number of households in the final survey sample produced the initial expansion

weight for each sampling segment and strata, as shown in Table 8.

TABLE 7: RESULTS OF INITIAL EXPANSION SUMMARY

SAMPLE SEGMENT OFM 2017 HHs COMPLETE HHs IN

SAMPLE

INITIAL EXPANSION

WEIGHT

City of Redmond 26,308 572 45.99

City of Seattle UVs 139,375 1,862 74.85

PSRC 1,415,993 851 1,663.91

TOTAL 1,581,676 3,285 481.54

16 Washington State Office of Financial Management (OFM) April 1, 2017 official population estimates https://www.ofm.wa.gov/washington-data-research/population-demographics/population-estimates/april-1-official-population-estimates

https://www.ofm.wa.gov/washington-data-research/population-demographics/population-estimates/april-1-official-population-estimates

https://www.ofm.wa.gov/washington-data-research/population-demographics/population-estimates/april-1-official-population-estimates


38

TABLE 8: RESULTS OF INITIAL EXPANSION WITHIN SAMPLING SEGMENTS

COMPLETED

HHs IN SAMPLE

% OFM 2017

HHs

INITIAL EXPANSION

WEIGHT

PSRC-regular-low 136 4.14 258,543 1,901.1

PSRC-regular-medium 204 6.21 446,272 2,187.6

PSRC-regular-high 222 6.76 437,706 1,971.6

PSRC-oversample-low 158 4.81 169,016 1,069.7

PSRC-oversample-medium 66 2.01 49,180 745.2

PSRC-oversample-high 65 1.98 55,276 850.4

Redmond-regular-medium 60 1.83 3,861 64.4

Redmond-regular-high 141 4.29 13,459 95.5

Redmond-oversample-low 75 2.28 1,972 26.3

Redmond-oversample-medium 36 1.10 1,762 48.9

Redmond-oversample-high 39 1.19 1,951 50.0

Redmond-downtown-medium 71 2.16 750 10.6

Redmond-downtown-high 149 4.54 2,553 17.1

Urban Village—10% 218 6.64 39,378 180.6

UV-15% 399 12.15 37,073 92.9

UV-20% 178 5.42 14,795 83.1

UV-25% 116 3.53 11,376 98.1

UV-30% 195 5.94 11,278 57.8

UV-35% 243 7.40 12,614 51.9

UV-50% 120 3.65 3,971 33.1

UV-60% 131 3.99 3,659 27.9

UV-75% 75 2.28 2,029 27.1

UV-100% 188 5.72 3,202 17.0

TOTAL 3285 100.00 1,581,676 481.5

6.3 | WEIGHTING TARGETS

Using the initial expansion factors shown in Table 8, the expanded sample matched the total number of

households for the PSRC four-county study region and for each sampling stratum. If there were no

nonresponse biases in survey recruitment and completion, then the initial expansion weights would be

accurate enough to obtain a representative weighted sample. However, inevitable biases and the inherent

randomness of sampling necessitated further adjustment of the expansion weights. To understand the

required adjustments, the initial expanded sample was compared against the following demographic

“target” dimensions and geographies were established. The geographical targets were set at the Public

39

Use Microdata Area (PUMA) level, which in the four-county PSRC region comprises 31 PUMAs (16

PUMAs in King County, two in Kitsap, seven in Pierce, and six in Snohomish). RSG adjusted the initial

expansion weights to match demographic control data targets from the ACS PUMS 1-year 2016 data for

the following target dimensions:

• Household Targets:

− Household size (1, 2, 3, 4, 5+).

− Number of workers (0, 1, 2, 3+).

− Income group (0-25k, 25-50k, 50-75k, 75-100k, 100-150k, 150k+).

− Number of vehicles (0, 1, 2, 3+).

− Age of head of household (under 35, 35-64, 65+).

• Person Targets:

− Total persons.

− Gender.

− Age (0-4, 5-15, 16-17, 18-24, 25-44, 45-64, 65+).

− Worker status (Worker, Nonworker).

− Race (White, Asian, Other).

− University/College student status (student, nonstudent).

The sample did not include a target for student status with school types other than university/college,

since the controls on the younger age groups (0–4, 5–15, 16–17) will already give a good estimate of

students in the K-12 grades; the university/college student target is intended to split the older age groups

into students and nonstudents. The race and university student targets are important as these groups are

often underrepresented in household travel survey samples.

To directly align with the initial expansion weights, RSG adjusted the 2016 ACS 1-year data target

variables using the WA State OFM 2017 population estimates of household totals. For each of the PSRC

region’s 31 PUMA geographies, the ratio of the ACS 2016 1-year households total to the OFM 2017

households total was derived and then applied as a scaling adjustment to all household and person target

variables accordingly, as shown in Table 9.


40

TABLE 9: PUMA HH TOTAL SCALE FROM ACS 2016 TO OFM 2017 ESTIMATES

PUMA ID ACS 2016 1 YEAR HH

TOTAL WA STATE OFM 2017

HH TOTAL SCALE

11501 50,951 51,378 1.008

11502 45,911 47,897 1.043

11503 44,835 42,092 0.939

11504 46,106 47,328 1.027

11505 45,309 43,981 0.971

11506 47,614 47,660 1.001

11507 36,945 39,647 1.073

11601 71,530 74,756 1.045

11602 53,305 54,372 1.020

11603 76,989 86,286 1.121

11604 55,365 54,874 0.991

11605 61,934 58,700 0.948

11606 47,047 48,627 1.034

11607 57,472 59,632 1.038

11608 57,596 58,679 1.019

11609 53,846 53,743 0.998

11610 53,129 53,238 1.002

11611 51,311 48,798 0.951

11612 48,664 48,608 0.999

11613 45,114 45,425 1.007

11614 41,558 41,003 0.987

11615 45,079 43,048 0.955

11616 41,212 40,650 0.986

11701 47,081 48,274 1.025

11702 45,482 48,350 1.063

11703 47,198 48,297 1.023

11704 45,616 46,409 1.017

11705 47,501 46,941 0.988

11706 53,238 52,318 0.983

11801 50,431 49,266 0.977

11802 51,563 51,399 0.997

TOTAL 1,566,932 1,581,676 1.009

41

6.4 | STEP 2. REWEIGHTING SURVEY HOUSEHOLDS TO EXPANSION

TARGETS

RSG completed the reweighting using list-based iterative proportional fitting (IPF), programmed in Pascal

and then translated the code into R. List-based IPF is like conventional IPF used in weighting but allows

for both household-level and person-level weighting variables to be included in a single list at the

household level, with the amount that each household contributes toward each target.

The IPF procedure looped over the demographic target dimensions (created from 2016 1-year PUMS

data and scaled to the WA State OFM 2017 HH totals), and the survey data with initial expansion

weights, and gradually adjusted the weights to match the target values. The program was run for 10,000

IPF iterations so that each target cell value is matched within 1%, with minimum weight ratio at 0.25x of

the initial expansion weight and a maximum weight ratio at 6x. It is RSG’s current judgment from

household travel survey weighting experience that the weights should not exceed 6x the initial expansion

weights to match the targets as closely as possible while avoiding extreme values.

After the initial IPF results were evaluated, RSG combined PUMA geographies in the PSRC region to

further refine the target geographies in consideration of adequate sample size in each geography. This

additional geographic refinement was based on ensuring adequate sample size of completed households

in similar demographic/geographic areas. Rather than allowing extreme weights, best practice is to

combine areas to reduce the effect of small cell sample sizes. The City of Redmond (PUMA 11607) was

oversampled, as noted in the sample plan, which resulted in adequate completed sample households to

remain a distinct PUMA target geography for regional weights.

TABLE 10: PUMA HH TOTAL SCALE FROM ACS 2016 TO OFM 2017 ESTIMATES

COMBINED PUMA GEOGRAPHY PUMAS

Seattle 11601

Seattle 11602

Seattle 11603

Seattle (Central) 11604,11605

Redmond 11607

Rest of King County 11606,11608,11609, 11610,11611, 11612,11613,11614,11615,11616

Pierce County and Kitsap County 11501,11502,11503,11504,11505,11506,11507,11801, 11802

Snohomish County 11701, 11702, 11703, 11704, 11705, 11706

As a result of the IPF reweighting process by adjusted PUMA groupings, the household weights were

created. The range of the household weights were from 2.65 to 13,126, with ~95% of the weights

between 2.65 to 1,250. The mean household weight was 482.6 with the median weight at 46.5, evidence

of the distribution of weights is right-skewed, as shown in Figure 11.


42

FIGURE 11: DISTRIBUTION OF FINAL HOUSEHOLDS WEIGHTS

After the household weights were created, the fit of each target variable was evaluated by plotting in an R

heatmap (GGPlot2) output. As shown in Figure 12, each cell illustrates the percentage fit of the

household weights multiplied by the sample size to match the target sum. For example, for the “h_total”

variable (households total) the sum of weighted households is shown to have 0% difference for the target

of WA State OFM 2017 households by the defined PUMA groupings.

FIGURE 12: PUMS-BASED TARGETS FOR EACH PUMA GROUP AND TARGET VARIABLE

6.5 | STEP 3. TRIP WEIGHTS AND TRIP CORRECTION FACTORS

The study collected travel diary data using rMove and the online travel diary. The final part of the

weighting process involved comparing and adjusting trip rates based on detectable biases from the two

data collection methods. RSG collected the data by smartphone using the rMove app and compared this

to the data collected using the traditional diary recall method online or over the telephone. Typically, the

smartphone-based data contain more days with travel and more trips per day and are used as the best

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

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0

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250

300

350

400

450

500

550

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Per

cen

t o

f W

eigh

ted

Ho

use

ho

lds

Final Household Weight

43

estimate of the true trip rates. The traditional diary-based data are adjusted so that the trip rates (and

percentage of days with no trips) more closely match the smartphone-based data.

Table 11 shows the number of valid person-days of data collected as part of complete household-days—

days on which complete data was collected for all household members. A total of 6,044 person-days are

from rMove respondents, spread evenly across all seven days of the week. A total of 5,019 person-days

are from the diary-based survey, which was administered for one travel day on a Tuesday, Wednesday,

or Thursday. By study design, there were 697 households that used smartphone versus 2,580 that used

travel diaries, so the multiday smartphone method yielded 8.50 valid person-days per household,

compared to 1.95 person-days per household for the one-day diary method. (On average, the rMove

households have 5.1 complete household-days of data.)

TABLE 11: NUMBER OF PERSON-DAYS AND AVERAGE LINKED TRIPS PER DAY, BY SURVEY METHOD AND DAY OF WEEK

PERSON-DAYS OF DATA

AVERAGE TRIPS PER PERSON-DAY

rMove Diary rMove Diary

TUE 960 1,545 4.02 3.50

WED 946 1,760 4.07 3.53

THU 873 1,714 4.16 3.42

FRI 854 -- 4.67 --

SAT 760 -- 4.73 --

SUN 834 -- 3.89 --

MON 817 -- 4.07 --

TOTAL 6,044 5,019 4.22 3.48

Table 11 shows that the rMove average linked trip rates for Monday, Tuesday, Wednesday, and

Thursday are all similar—in the range 4.02 to 4.16, while the Friday trip rate is much higher at 4.67. This

is due to more frequent evening activities on Fridays. For trip rate comparison and adjustment, Friday is

excluded along with Saturday and Sunday. Only Monday-Thursday are used as these are stable and

comparable to the Tuesday-Thursday period used for the diary method.

Table 12 contains the results of analyses to compare the travel patterns for the rMove and traditional

diary-based data. RSG split the traditional diary households into four groups:

• “Opt-out”: All HH adults own smartphones, but the household opted out of using rMove.

• “NO-all”: All HH adults own smartphones, but the household was not given an option to use

rMove.

• “NO-some”: Some HH adults own smartphones, so the HH was not given an option to use rMove.

• “NO-none”: No HH adults own smartphones, so the HH was not given an option to use rMove.

These were compared to the “Opt-in” group who opted to use rMove. Note that the “NO-all” group is likely

to be most similar to the “Opt-in” group because all adults own smartphones and many of these

households would have used rMove if they had been given the option. The “Opt-out” group, on the other


44

hand, chose not to use rMove, so may be different from the “Opt-in” group in ways that also influence

travel behavior.

The first part of Table 12 shows the number of Monday-Thursday person-days of data in each group. In

sum, 42% of the days are in the rMove group, while between 11% and 18% of the person-days are in

each of the four diary-based groups. The average trips per day in the rMove Monday-Thursday person-

days is 4.08. The average trip rate for the most similar diary-based group (NO-all) is 3.68, which is about

10% lower than for rMove. The other three diary-based groups have somewhat lower trip rates, but that is

likely due to characteristics that are correlated to being in the opt-out and non-smartphone-owning groups

(e.g., older, lower income)

The maximum number of trips in any of the diary-based person-days is 17, while the rMove person-days

include as many as 64 trips, with multiple cases of over 20 trips. However, capping the number of rMove

trips per day at a maximum of 20 for analysis purposes only reduces the mean rMove trips rate by 4.08 to

4.05—less than 1%. So, this analysis is not greatly affected by extreme cases, but it will be important to

truncate the extreme cases for any regression analyses, which are much more sensitive to outlier effects.

For rMove surveys in the past, we have found that one of the main reasons for higher trip rates is that

there are fewer “stay-at-home” days with zero trips as compared to diary-based data. Table 12 shows that

this is true for the PSRC data, with only 6.1% zero-trip days in the rMove data compared to 10.5% in the

two diary-based groups with high smartphone ownership, and about 18% in the two diary-based groups

with partial or no smartphone ownership (which tend to be older, nonworking households). When the

number of trips is averaged only across days with 1+ trips, the average trip rate for rMove person-days

goes up to 4.38, while for the diary-based groups it is 4.11 for the NO-all group, so the difference is

reduced from 10% to 6%. This result indicates that about half of the higher trip rate for rMove is due to a

lower frequency of nontravel days, while the rest is due to a higher number of trips on days with travel.

The remaining sections of Table 12 investigate the differences across the groups in three ways: 1) by the

number of work and school tours, the number of other home-based tours, and the number of intermediate

stops person-day; 2) by the number of trips by mode; and 3) by the distance traveled by mode. (The

distances are based on the GPS traces for rMove trips and from the Google API for diary-based trips. All

trip distances were truncated at 100 miles to avoid undue effects of long trips. These values are

calculated across all (Monday-Thursday) person-days, and then again across days with one or more trips

only, excluding the “stay-at-home” days.

Some key observations based on the averages across all days include the following:

• When compared to the “Opt-out” and “NO-all” groups, the rMove group has a similar average

number of work/school and other tours, but approximately 40% more non-home-based trips,

meaning more intermediate stops on tours.

• When compared to the “Opt-out” and “NO-all” groups, the rMove group has a similar (or lower)

number of walk/bike and “other mode” trips, but approximately 18% more car trips, and somewhat

more transit trips.

• While the main discrepancy between the groups is in car trips, the average miles traveled by car

per day is similar across the groups. This suggests that while rMove is capturing extra additional

intermediate stops on auto tours, those stops are not adding much in terms of total auto distance

45

traveled, so these data may not be as critical in terms of transportation demand. If trip correction

were done without taking this into account, it might “overcorrect” and create too much extra auto

mileage in the diary-based trips.

• The average distance per day for walk/bike is higher in the rMove group, even though the average

number of trips is not higher. This may be because rMove is better at capturing recreational “loop

trips” by those modes, which may tend to be longer distance than other walk/bike trips. This

difference may warrant further investigation before the data are used for modeling purposes, but it

was not be a focus of the trip correction process.

Some key observations based on the averages across days with 1+ trips only include the following:

• Compared to the “Opt-out” and “NO-all” groups, the rMove group has a similar (or slightly lower)

average number of work/school and other tours. It still has more non-home-based trips, but now

the difference is only about 25% rather than 40% (1.65 NHB trips per day in the rMove group vs.

1.26 in the NO-all group).

• Compared to the “Opt-out” and “NO-all” groups, the rMove group has a similar (or lower) number

of walk/bike and “other mode” trips. It still has more car trips, but the difference is approximately

12% instead of 18% (2.77 auto trips per day in the rMove group vs. 2.44 in the NO-all group).

• The average miles traveled by car per day is similar across the groups—and somewhat lower in

the rMove group than in the other groups. (Some of the difference may be attributable to the way

that the distance measures are obtained, but they should be comparable.) The average miles

traveled across all modes is almost identical across the smartphone-owning groups.

TABLE 12: ANALYSIS OF TRIP RATES AND TRAVEL PATTERNS, BY RESPONDENT GROUP

LABEL OPT-IN OPT-OUT NO-ALL NO-SOME NO-NONE TOTAL

Survey method used rMove Diary Diary Diary Diary --

Offered rMove option? Yes Yes No No No --

HH adults own smartphones?

All All All Some None --

Person-days of data (Mon-Thu)

3,596 1,531 1,452 1,096 940 8,615

% of person-days (Mon-Thu)

42% 18% 17% 13% 11% 100%

Average trips per day (Mon-Thu)

4.08 3.53 3.68 3.23 3.40 3.72

% of days with no trips 6.1% 10.5% 10.5% 17.3% 18.1% 10.4%

Average trips on days w/ 1+ trips

4.35 3.94 4.11 3.91 4.15 4.15

Avg. work & school tours 0.66 0.69 0.70 0.57 0.45 0.64

Avg. other home-based tours

0.63 0.59 0.62 0.62 0.74 0.63

Avg. non-home-based trips

1.54 1.05 1.13 0.91 1.05 1.25


46

LABEL OPT-IN OPT-OUT NO-ALL NO-SOME NO-NONE TOTAL

Avg. total tours 1.29 1.28 1.32 1.19 1.19 1.27

Avg. trips per tour 3.19 2.82 2.85 2.76 2.88 2.99

Avg. trips by car 2.60 2.16 2.19 2.17 2.06 2.34

Avg. trips by walk or bike 0.92 0.92 0.99 0.65 0.88 0.89

Avg. trips by transit 0.45 0.36 0.40 0.32 0.39 0.40

Avg. trips by other modes

0.11 0.09 0.11 0.08 0.06 0.10

Avg. miles by car 14.6 14.7 14.2 15.7 13.0 14.5

Avg. miles by walk or bike

1.3 0.8 0.8 0.6 0.7 0.9

Avg. miles by transit 3.0 2.5 2.8 2.6 2.5 2.8

Avg. miles by other modes

1.4 1.4 1.6 1.2 0.7 1.3

Avg. total miles 20.3 19.4 19.4 19.8 17.0 19.6

Only including days with 1+ trips

Avg. work & school tours 0.70 0.77 0.79 0.69 0.55 0.71

Avg. other home-based tours

0.67 0.66 0.69 0.75 0.91 0.70

Avg. non-home-based trips

1.65 1.18 1.26 1.10 1.29 1.40

Avg. total tours 1.37 1.43 1.38 1.44 1.46 1.41

Avg. trips per tour 3.19 2.82 2.85 2.76 2.88 2.99

Avg. trips by car 2.77 2.42 2.44 2.62 2.52 2.61

Avg. trips by walk or bike 0.98 1.03 1.10 0.79 1.08 1.00

Avg. trips by transit 0.48 0.40 0.44 0.39 0.47 0.45

Avg. trips by other modes

0.12 0.10 0.12 0.10 0.08 0.11

Avg. miles by car 15.6 16.4 15.9 19.0 15.9 16.2

Avg. miles by walk or bike

1.3 0.9 0.9 0.7 0.9 1.1

Avg. miles by transit 3.2 2.8 3.1 2.8 3.2 3.1

Avg. miles by other modes

1.5 1.6 1.8 1.5 0.8 1.5

Avg. total miles 21.7 21.7 21.7 24.0 20.8 21.8

47

TRIP RATE ADJUSTMENT

The analysis described above indicates that the discrepancies in the trip rates between the rMove data

and traditional diary-based data from comparable households arise from two main sources:

• A higher percentage of “stay-at-home” days in the self-reported traditional diary data, some of

which may be “soft refusals” where respondents reported making no trips on days when they

traveled.

• A higher number of non-home-based trips per day in the rMove data, mainly occurring on auto

tours. These extra intermediate stops on auto tours mainly have the effect of splitting auto tours

into additional, shorter auto trips, without significantly adding to the total auto distance traveled on

the tours.

These findings are consistent with what RSG has found in similar comparative analyses from past

surveys in other regions in the United States.

The standard trip rate adjustment process using GPS-based data is to weight the trip records directly,

using different bias correction factors for different types of trips. The trip records in the data were cross-

classified by three dimensions:

1. Trip OD purpose: (a) Home-based mandatory (work, school), (b) Home-based other, and (c)

non-home-based.

2. Trip mode type: (a) Auto, (b) Walk/bike, and (c) Transit/other.

3. Trip distance quartile: (a) 0-1 miles, (b) 1-3 miles, (c) 3-8 miles, and (d) over 8 miles.

Based on an analysis of the distribution of different trip type combinations across the different respondent

types, the trips were classified into six categories:

• Home-based mandatory—all modes and distances.

• Other OD purposes—walk/bike trips, all distances.

• Other OD purposes—transit/other trips, all distances.

• Other OD purposes—auto trips, 0-1 miles.

• Other OD purposes—auto trips, 1-8 miles.

• Other OD purposes—auto trips, over 8 miles.

The classification was determined by looking for groups of combinations with sufficient numbers of trips to

make a valid comparison between data types and looking for combinations that showed different

percentages of trips between the Opt-in rMove respondents and comparable smartphone-owning

households. Table 13 shows that relative to the Opt-in group, the NO-all group has more home-based

mandatory trips and fewer short auto trips for other purposes, with similar fractions for the other

categories. (These values are weighted by the household-level weights.)


48

TABLE 13: PERCENTAGE OF TRIPS BY OD PURPOSE/MODE/DISTANCE COMBINATION AND RESPONDENT TYPE (DATA WEIGHTED BY HOUSEHOLD-LEVEL WEIGHTS)

LABEL OPT-IN OPT-OUT

NO-ALL

NO-SOME

NO-NONE

TOTAL



HH adults own smartphones? All All All Some None --

HB Mandatory—all modes and distance 19.5% 33.0% 29.0% 29.2% 17.8% 24.9%

Other purposes—all walk and bike trips 9.4% 12.9% 10.4% 8.5% 15.4% 10.7%

Other purposes—all transit and other trips

3.2% 1.6% 3.1% 5.5% 6.3% 3.5%

Other purposes—Auto trips—0-1 miles 16.5% 8.9% 8.4% 13.9% 11.4% 12.7%

Other purposes—Auto trips—1-8 miles 37.5% 28.0% 35.9% 31.6% 38.6% 34.8%

Other purposes—Auto trips—over 8 miles

14.0% 15.6% 13.0% 11.3% 10.5% 13.5%

TOTAL 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Next, the trips were aggregated by person-day across these same categories, to determine the number of

trips per weekday person-day in each category, when weighted by the household-level weights. Table 14

shows the results. A comparison of the results for the Opt-in and NO-all groups indicates that the number

of auto and transit trips for Other purposes should be weighted up—particularly for the short auto trips—

enough to make up for the difference in trips between the Opt-in and NO-all groups. The adjustment

factors and results after adjustment are shown in Table 15. The number of trips per day in the Opt-in and

NO-all groups now match closely.

49

TABLE 14: AVERAGE TRIPS BY PERSON-DAY BY OD PURPOSE/MODE/DISTANCE COMBINATION AND RESPONDENT TYPE

LABEL OPT-

IN OPT-OUT

NO-ALL

NO-SOME

NO-NONE

TOTAL



HH adults own smartphones? All All All Some None --

HB Mandatory—all modes and distance 0.90 1.04 1.17 0.83 0.52 0.92

Other purposes—all walk and bike trips 0.44 0.41 0.42 0.24 0.45 0.40

Other purposes—all transit and other

trips 0.15 0.05 0.12 0.16 0.18 0.13

Other purposes—Auto trips—0-1 miles 0.77 0.28 0.34 0.39 0.33 0.47

Other purposes—Auto trips—1-8 miles 1.74 0.88 1.45 0.90 1.12 1.29

Other purposes—Auto trips—over 8

miles 0.65 0.49 0.52 0.32 0.30 0.50

TOTAL 4.65 3.15 4.02 2.84 2.90 3.71

TABLE 15: AVERAGE TRIPS BY PERSON-DAY BY OD PURPOSE/MODE/DISTANCE COMBINATION AND RESPONDENT TYPE AFTER ADJUSTMENT

LABEL OPT-

IN OPT-OUT

NO-ALL

NO-SOME

NO-NONE

TOTAL ADJUSTMENT FACTOR-

Survey method used rMove Diary Diary Diary Diary -- --

Offered rMove option? Yes Yes No No No -- --

HH adults own smartphones?

All All All Some None -- --

HB Mandatory—all modes and distance

0.90 1.04 1.17 0.83 0.52 0.92 1.00

Other purposes—all walk and bike trips

0.44 0.41 0.42 0.24 0.45 0.40 1.00

Other purposes—all transit and other trips

0.15 0.06 0.14 0.18 0.21 0.15 1.15

Other purposes—Auto trips—0-1 miles

0.77 0.56 0.68 0.79 0.66 0.94 2.00

Other purposes—Auto trips—1-8 miles

1.74 1.01 1.67 1.04 1.29 1.48 1.15

Other purposes—Auto trips—over 8 miles

0.65 0.57 0.60 0.37 0.35 0.57 1.15

TOTAL 4.65 3.65 4.67 3.44 3.47 4.47 --


50

6.6 | FINAL WEIGHTS AND RECOMMENDED USE

The weights provided with the 2017 PSRC household travel survey data are as follows:

• hh_wt_final: The resulting weights from the weighting to the ACS data. This weight should be

used for household-level and person-level analyses.

• hh_day_wt_final: The same as hh_wt_final but divided by the number of complete weekdays of

data (Monday-Thursday) for each household. This weight should be used for household-day and

person-day-level analyses, as it represents an average weekday.

• trip_wt_final: The resulting weights from the trip correction factor process described in the

previous section. This is equal to hh_day_wt_final multiplied by the trip correction factor in Table

15 for each trip type in the diary-based data.

The final round of updating and checking the weights excluded eight households whose trip data were

determined to be invalid. Table 16 to Table 20 show the resulting unweighted and weighted totals for

those final weights at household, person, person-day, and trip-level. In each case, the recommended

weight to use is highlighted, so that the number of households or persons across the entire sample match

the ACS-based targets. Separating out the rMove respondents by the number of weekdays of data shows

how the hh_day_wt_final scales down the weights for the multiday households. This is appropriate for

day-level and trip-level data, but not for household-level and person-level data. The number of trips per

person-day in the diary-based data is still less than in the smartphone-based data even after correction

because most of the difference is due to demographic differences between the samples using each

method.

TABLE 16: NUMBERS OF HOUSEHOLDS, UNWEIGHTED AND WEIGHTED

HOUSEHOLDS UNWEIGHTED HH_WT_FINAL

Diary 2,580 1,332,898

smartphone-1 weekday 71 31,712

smartphone-2 weekdays 109 52,494



TOTAL 3,277 1,581,620

TABLE 17: NUMBERS OF PERSONS, UNWEIGHTED AND WEIGHTED

PERSONS UNWEIGHTED HH_WT_FINAL

Diary 5,019 3,309,037





TOTAL 6,235 3,850,152

51

TABLE 18: NUMBERS OF PERSON-DAYS, UNWEIGHTED AND WEIGHTED

PERSON-DAYS UNWEIGHTED HH_DAY_WT_FINAL

Diary 5,019 3,309,037




smartphone-4 weekdays 2,292 237,009

TOTAL 8,769 3,850,152

The data file includes 35,024 (Group 1) rMove trips, plus 17,469 diary-based trips (Group 2), for a total of

52,493 trips in the trip file. Of the rMove trips 8,756 were on incomplete household-days (one or more

persons had an incomplete survey day), and 11,207 were on Fri-Sun, leaving 15,061 rMove trips on

complete weekdays (Mon-Thu). Those 15,061 rMove trips (Group 1) have nonzero weights and are

included in Table 19 and Table 20.

TABLE 19: NUMBERS OF WEEKDAY TRIPS, UNWEIGHTED AND WEIGHTED

TRIPS UNWEIGHTED TRIP_WT_FINAL

Diary 17,469 12,754,413




smartphone-4 weekdays 9,026 1,183,296

TOTAL 32,530 15,432,320

TABLE 20: NUMBERS OF WEEKDAY TRIPS PER PERSON-DAY, UNWEIGHTED AND WEIGHTED

TRIPS PER PERSON-DAY UNWEIGHTED TRIP_WT_FINAL

Diary 3.48 3.85

smartphone-1 weekday 4.41 4.85

smartphone-2 weekdays 4.27 5.05



TOTAL 3.71 4.01


52

7.0 SURVEY RESULTS

7.1 | SURVEY RESPONSE RATES

The study exceeded its target of 3,100 households with 3,277 households who completed the study

throughout the 4-county PSRC region. Figure 13 maps the home locations of participants for the rMove

smartphone participants (Group 1) and the rSurvey online diary participants (Group 2) complete

households.

FIGURE 13: PARTICIPANT HOME LOCATIONS, BY PARTICIPATION GROUP (RMOVE HOUSEHOLDS ON LEFT, RSURVEY HOUSEHOLDS ON RIGHT)

As is typical, PSRC is releasing a public version of their dataset on the agency website. The dataset

contains 3,285 households. However, we note here that during the process of weighting (as noted in the

section above) a very small number of households were excluded (and thus have weights of zero). These

eight households are noted below by their ID as a reference. Again, the weighting section above and this

section of the report are based on the final 3,277 households used in weighting.

• 17102275

• 17109128

• 17121270

• 17123255

• 17126685

• 17131745

• 17136275

• 17138989

As noted in the previous Survey Design section, Part 1 of the study included two questions about

smartphone ownership. Participants over age 18 were asked to specify what type of smartphone they had

(if any) and the phone’s age. RSG then used this information to determine group assignments. Groups

53

were assigned after each household completed Part 1 (the demographic and household information

section). The goal at the start of the study was to recruit approximately 20% of total households for Group

1. The result was 21.3% of complete households used rMove to collect data (Group 1), with 78.7% of

completing via the online diary (Group 2) as seen in Table 21. Group 1 participants reported their travel

for seven days using rMove, while Group 2 participants reported their travel for one-day online using

rSurvey.

TABLE 21: HOUSEHOLDS BY PARTICIPATION GROUP

PARTICIPATION GROUP UNWEIGHTED WEIGHTED

COMPLETE HHs

PERCENT FREQUENCY PERCENT

Group 1: rMove only 697 21.3% 248,721 15.7%

Group 2: Online diary only 2,580 78.7% 1,332,898 84.3%

TOTAL 3,277 100.0% 1,581,620 100.0%

As noted in detail in the sampling section, RSG first established the target sample sizes for each region.

This was done to achieve the sampling goals for each of the sponsoring agencies. The contracted sample

sizes were determined within and by each agency based on long-term data needs. The primary sampling

frame was the list of all households in the four-county study region (King, Kitsap, Pierce, and Snohomish

counties). RSG used ABS to select and invite households to participate in the study. ABS involved

drawing a random sample of addresses from all the residential addresses in each defined geography

such that all households in each defined geography have an equal chance of selection for the sample.

Table 22 and Table 23 show the results of the survey sampling plan with the number of invites per

sample segment, with subsequent recruited households, complete households (by participation Group 1

rMove / Group 2 online diary), response rate and sample rate.

TABLE 22: SURVEY RESPONSE SUMMARY

SAMPLE SEGMENT

TOTAL HHs ACS

2011-15 INVITES

RECRUITED HHs

RECRUIT RATE

COMPLETE HHs

RESPONSE RATE

PSRC 1,353,823 19,150 1,224 6.4% 849 4.4%

City of Redmond 24,398 7,590 776 10.2% 571 7.5%

City of Seattle UVs 117,421 27,473 2,548 9.3% 1,857 6.8%

TOTAL 1,495,642 54,213 4,548 8.4% 3,277 6.0%


54

TABLE 23: HOUSEHOLD SURVEY RESPONSE BY SAMPLE SEGMENT

SAMPLE SEGMENT TARGET SAMPLE

SIZE

PREDICTED RESPONSE

RATE INVITES

RECRUITED HHs

COMPLETE HHs IN

SAMPLE

RESPONSE RATE

PSRC:

(01) low response rate 149 4.35% 3,440 182 136 3.95%

(02) med. response rate 255 5.70% 4,510 292 203 4.50%

(03) high response rate 252 7.60% 3,330 328 222 6.67%


(12) med. response rate 69 5.70% 1,220 95 66 5.41%


PSRC Total 1,046 -- 19,150 1,224 849 4.43%

Redmond:

(22) med. response rate 43 5.70% 750 72 60 8.00%




(33) high response rate 48 7.60% 640 54 39 6.09%



Redmond Total 500 -- 7,590 776 571 7.52%

Seattle Urban Villages:

(50) 10% 238 7.95% 3,000 281 218 7.27%

(51) 15% 339 6.85% 4,950 558 398 8.04%

(52) 20% 151 5.78% 2,620 243 177 6.76%

(53) 25% 95 4.32% 2,210 154 115 5.20%

(54) 30% 199 6.52% 3,060 291 194 6.34%

(55) 35% 196 4.77% 4,110 341 244 5.94%

(56) 50% 102 6.87% 1,490 160 120 8.05%

(57) 60% 92 4.76% 1,940 167 130 6.70%

(58) 75% 87 6.49% 1,350 109 74 5.48%

(59) 100% 141 5.11% 2,743 244 187 6.82%

Urban Village Total 1,640 -- 27,473 2,548 1,857 6.76%

TOTAL (Households) 3,186 -- 54,213 4,548 3,277 6.04%

55

7.2 | HOUSEHOLD RESULTS

The three most key household-level variables are household income, household size, and household

vehicle ownership. Many of the results below are shown as percentages of the overall number of

complete households (3,277). Because the participation group (rMove smartphone-based or rSurvey

online travel diary) was a key factor in how the data were collected—in addition to being a newer and

innovative data collection practice—some figures are segmented by participation group.

Results by household size are an important measure for HTS projects. Like the majority of household-

level studies, this project’s participation was skewed toward smaller households. This is primarily due to

the fact that it is less burden for smaller households to participate in and complete the study. RSG

corrected this bias in the weighting process. Importantly, there were relatively few observed differences in

household characteristics for households that used rMove (Group 1) and those that did not (Group 2).

Note also because time passed over the course of the project, an impact is that the original sample plan

used 2011-2015 ACS data, whereas the weighted results below use the 2012-2016 ACS data.

TABLE 24: HOUSEHOLD SIZE, BY PARTICIPATION GROUP (UNWEIGHTED)

NUMBER OF PERSONS

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

1 person 328 47.1% 980 38.0% 1,308 39.9%

2 people 265 38.0% 1076 41.7% 1,341 40.9%

3 people 62 8.9% 275 10.7% 337 10.3%

4 people 39 5.6% 205 7.9% 244 7.4%

5 or more people 3 0.4% 44 1.7% 47 1.4%

TOTAL (Households)

697 100.0% 2,580 100.0% 3,277 100.0%

TABLE 25: HOUSEHOLD SIZE, BY PARTICIPATION GROUP (WEIGHTED)

NUMBER OF PERSONS

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

1 person 99,475 40.0% 343,233 25.8% 442,708 28.0%

2 people 78,668 31.6% 472,612 34.9% 551,280 34.9%

3 people 20,927 8.4% 229,967 15.9% 250,894 15.9%

4 people 38,014 15.3% 163,537 12.7% 201,552 12.7%

5 or more people 11,637 4.7% 123,549 9.3% 135,186 8.5%

TOTAL (Households)

248,721 100.0% 1,332,898 100.0% 1,581,620 100.0%

Next, the tables below present the unweighted and weighted summary of self-reported household income

across participation groups. Higher-income households are more likely to own smartphones, therefore

affecting study participation methods. Both groups show an underrepresentation of the lowest income


56

household group (under $25,000) when comparing the survey unweighted to the weighted results.

Notably between the groups, Group 2 online survey households were almost twice as likely as Group 1

rMove households to select prefer not to answer rather than report income (7.9% vs 4.2%)

TABLE 26: HOUSEHOLDS BY INCOME GROUPAND PARTICIPATION GROUP (UNWEIGHTED)

HOUSEHOLD INCOME

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

Under $25,000 66 9.5% 284 11.0% 350 10.7%

$25,000 to $49,999 106 15.2% 392 15.2% 498 15.2%

$50,000 to $74,999 114 16.4% 367 14.2% 481 14.7%

$75,000 to $99,999 114 16.4% 304 11.8% 418 12.8%

$100,000 or more 268 38.5% 1028 39.8% 1296 39.5%

Prefer Not to Answer 29 4.2% 205 7.9% 234 7.1%

TOTAL (Households) 697 100.0% 2,580 100.0% 3,277 100.0%

TABLE 27: HOUSEHOLDS BY INCOME GROUP AND PARTICIPATION GROUP WITH INCOME IMPUTATION (WEIGHTED)

HOUSEHOLD INCOME

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

Under $25,000 35,894 14.4% 188,336 14.1% 224,229 14.2%

$25,000 to $49,999 45,332 18.2% 231,559 17.4% 276,891 17.5%

$50,000 to $74,999 46,363 18.6% 222,617 16.7% 268,980 17.0%

$75,000 to $99,999 42,379 17.0% 177,262 13.3% 219,640 13.9%

$100,000 to $149,999 16,791 6.8% 269,282 20.2% 286,073 18.1%

$150,000 or more 61,963 24.9% 243,843 18.3% 305,806 19.3%

TOTAL (Households) 248,721 100.0% 1,332,898 100.0% 1,581,620 100.0%

Table 27 and 28 show the unweighted and weighted summary of household vehicle ownership, reflecting

that 0-vehicle households were over-represented when comparing the survey unweighted to the weighted

results for both groups. This is likely due to the higher sampling rates in the City of Seattle and the City of

Redmond, where households are more likely to have no cars.

57

TABLE 28: HOUSEHOLDS BY VEHICLES AND PARTICIPATION GROUP (UNWEIGHTED)

HOUSEHOLD VEHICLES

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

0 (no vehicles) 139 19.9% 397 15.4% 536 16.4%

1 vehicle 361 51.8% 1228 47.6% 1589 48.5%

2 vehicles 160 23.0% 747 29.0% 907 27.7%

3 or more vehicles 37 5.3% 208 8.1% 245 7.5%

TOTAL (Households) 697 100.0% 2580 100.0% 3277 100.0%

TABLE 29: HOUSEHOLDS BY VEHICLES AND PARTICIPATION GROUP (WEIGHTED)

HOUSEHOLD VEHICLES

GROUP 1: RMOVE

HHs

GROUP 1: RMOVE %

GROUP 2: ONLINE

HHs

GROUP 2: ONLINE %

TOTAL: HHs

TOTAL: %

0 (no vehicles) 25,737 10.3% 104,709 7.9% 130,446 8.2%

1 vehicle 107,281 43.1% 389,993 29.3% 497,273 31.4%

2 vehicles 84,934 34.1% 508,560 38.2% 593,495 37.5%

3 or more vehicles 30,769 12.4% 329,636 24.7% 360,405 22.8%

TOTAL (Households) 248,721 100.0% 1,332,898 100.0% 1,581,620 100.0%

7.3 | PERSON RESULTS

Person-level data collected in the study included key elements like age, ethnicity, gender, employment,

and educational enrollment. As a reminder, by design, some data were collected differently based on if

the participant was a child or not. For example, ethnicity was not asked of children, only adults in the

household. Table 30 presents the persons by age group and participation group. There are more younger

people and fewer older people participating via smartphone than online/telephone, due to ownership and

use of smartphones by these different age groups. There are few differences in gender between the

participation groups and an almost equal match in male and female participation, as shown in Table 31.


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TABLE 30: PERSONS BY AGE GROUP AND PARTICIPATION GROUP (UNWEIGHTED)

PERSONS GROUP 1: RMOVE

PERSONS

GROUP 1: RMOVE %

GROUP 2: ONLINE DIARY

PERSONS

GROUP 2: ONLINE DIARY %

TOTAL: PERSONS

TOTAL: %

Under 5 years 69 5.7% 278 5.5% 347 5.6%

5-11 years 58 4.8% 249 5.0% 307 4.9%

12-15 years 20 1.6% 103 2.1% 123 2.0%

16-17 years 14 1.2% 60 1.2% 74 1.2%

18-24 years 85 7.0% 285 5.7% 370 5.9%

25-34 years 442 36.3% 1270 25.3% 1712 27.5%

35-44 years 245 20.1% 902 18.0% 1147 18.4%

45-54 years 137 11.3% 553 11.0% 690 11.1%

55-64 years 92 7.6% 582 11.6% 674 10.8%

65-74 years 50 4.1% 501 10.0% 551 8.8%

75-84 years 4 0.3% 198 3.9% 202 3.2%

85 years or older 0 0.0% 38 0.8% 38 0.6%

TOTAL (Persons) 1216 100.0% 5019 100.0% 6235 100.0%

TABLE 31: PERSONS BY GENDER AND PARTICIPATION GROUP (UNWEIGHTED)

GENDER GROUP GROUP 1: RMOVE

PERSONS

GROUP 1: RMOVE %


PERSONS


TOTAL: PERSONS

TOTAL: %

Male 600 49.3% 2409 48.0% 3009 48.3%

Female 600 49.3% 2487 49.6% 3087 49.5%

Another 3 0.2% 17 0.3% 20 0.3%

Prefer not to answer

13 1.1% 106 2.1% 119 1.9%

TOTAL (Persons) 1216 100.0% 5019 100.0% 6235 100.0%

There are a few notable differences in the participation by race/ethnicity, as presented in Table 32.

Whites tended to participate at a higher rate in the rMove survey than in the online diary. This does not

correlate directly with smartphone ownership in general, according to data for all adults in the U.S. from

the Pew research center where 66% of whites own a smartphone, but 68% of blacks and 64% of Hispanic

persons own a smartphone17. This same research shows a smartphone ownership of 30% for persons

over 65 years of age, correlating with retired persons in Table 33, which shows a higher percentage of

retired persons using the online diary than the smartphone application to participate in the study.

Employed persons have a higher rate of using rMove, both for full-time and part-time work. Pew also

17 http://www.pewinternet.org/2015/10/29/the-demographics-of-device-ownership/

http://www.pewinternet.org/2015/10/29/the-demographics-of-device-ownership/

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reports 81% for college or higher educational attainment, consistent with Table 34 that shows a higher

number of people in the rMove participation group than in the online diary.

TABLE 32: PERSONS (ADULTS ONLY) BY RACE/ETHNICITY AND PARTICIPATION GROUP (UNWEIGHTED) – SELECT ALL THAT APPLY QUESTION

RACE GROUP GROUP 1: RMOVE

PERSONS

GROUP 1: RMOVE %


PERSONS


TOTAL: PERSONS

TOTAL: %

African American or Black

22 2.1% 129 3.0% 151 2.8%

American Indian or Alaska Native

12 1.1% 44 1.0% 56 1.0%

Asian 172 16.3% 764 17.6% 936 17.4%

Native Hawaiian or Pacific Islander

8 0.8% 32 0.7% 40 0.7%

Hispanic 52 4.9% 160 3.7% 212 3.9%

White 792 75.1% 2974 68.7% 3766 69.9%

Other 23 2.2% 90 2.1% 113 2.1%

Prefer not to answer

32 3.0% 371 8.6% 403 7.5%

TOTAL 1,113 105.5% 4,564 105.4% 5,677 105.4%

Total Persons Age 18+

1,055 4,329 5,384

Total Persons <Age 18 (Not Asked Question)

161 690 851


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TABLE 33: PERSONS BY EMPLOYMENT STATUS AND PARTICIPATION GROUP (UNWEIGHTED)

EMPLOYMENT STATUS

GROUP 1: RMOVE

PERSONS

GROUP 1: RMOVE %


PERSONS


TOTAL: PERSONS

TOTAL: %

Employed full-time (35+ hours/week, paid)

717 59.0% 2367 47.2% 3084 49.5%

Employed part-time (<35 hours/week, paid)

97 8.0% 342 6.8% 439 7.0%

Self-employed 69 5.7% 275 5.5% 344 5.5%

Unpaid volunteer or intern

10 0.8% 42 0.8% 52 0.8%

Homemaker 46 3.8% 229 4.6% 275 4.4%

Retired 52 4.3% 705 14.0% 757 12.1%

Not currently employed 64 5.3% 369 7.4% 433 6.9%

Under 18 161 13.2% 690 13.7% 851 13.6%

TOTAL (Persons) 1216 100.0% 5019 100.0% 6235 100.0%

TABLE 34: PERSONS BY STUDENT TYPE AND PARTICIPATION GROUP (UNWEIGHTED)

STUDENT STATUS GROUP 1: RMOVE

PERSONS

GROUP 1: RMOVE %


PERSONS


TOTAL: PERSONS

TOTAL: %

Daycare 14 1.2% 52 1.0% 66 1.1%

Preschool 25 2.1% 94 1.9% 119 1.9%

K-12 public school 68 5.6% 325 6.5% 393 6.3%

K-12 private school 17 1.4% 62 1.2% 79 1.3%

K-12 home school 3 0.2% 24 0.5% 27 0.4%

College, graduate, or professional school

97 8.0% 307 6.1% 404 6.5%

Vocational / technical school

7 0.6% 20 0.4% 27 0.4%

None 34 2.8% 141 2.8% 175 2.8%

Other 5 0.4% 32 0.6% 37 0.6%

Not a student 946 77.8% 3962 78.9% 4908 78.7%

TOTAL (Persons) 1216 100.0% 5019 100.0% 6235 100.0%

7.4 | TRIP RATES AND TRAVEL ANALYSIS RESULTS

For the 3,277 complete households with 6,235 persons in the PSRC 2017 data, there were 35,024

smartphone-based trips and 17,469 diary-based trips, for a total of 52,493 trips (Table 35). Of the

smartphone-based trips (Group 1) 8,756 were incomplete household-days (one or more persons had an

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incomplete survey day), and 11,207 were on Friday, Saturday, or Sunday, resulting in 15,061

smartphone-based trips on complete weekdays (Monday -Thursday). The 11,207 Friday, Saturday or

Sunday trips remain in the trip dataset as zero-trip weight, with the 15,061 complete weekdays (Monday –

Thursday) have nonzero weights.

TRIP RATES

TABLE 35: NUMBERS OF HOUSEHOLDS, PERSONS AND TRIPS BY PARTICIPANT GROUP (WEIGHTED).

PARTICIPATION GROUP

COMPLETE HHs

PERSONS TRIPS HHs

WEIGHTED

WEEKDAY (MON.-THUR.) TRIPS

WEIGHTED

Group 1: rMove only 697 1,216 35,024 248,721 2,677,907

Group 2: Online diary only 2,580 5,019 17,469 1,332,898 12,754,413

TOTAL 3,277 6,235 52,493 1,581,620 15,432,321

The PSRC 2017 dataset was compared to the previously published PSRC 2006 and 2014 dataset reports

for weighted distributions. The 2017 weighted trip data had a slightly higher trip rate 9.76 trips per

household with 4.01 trips per day as shown in Table 36, (“trip_wt_final” weight as described previously in

Table 20). When separating out the data collection participations group households in the 2017 data the

Group 1 households had 4.95 trips per day for Group 1 for 1 weekday and 3.85 trips per day for Group 2,

again one weekday. Group 1 rMove smartphone households had the highest trip rate captured of all

recent PSRC surveys at 10.77 trips/household, which is comparable to the GPS adjusted trip rate from

2006. The household size for the rMove participants (group 1) was lower than the comparative surveys,

so the trips per person were slightly higher than any other survey. Due to the smartphone passive rMove

data collection technology with seven days of travel, the Group 1 households participation method

recorded active trips for nearly every person and households (<0.1%), while households and persons

reporting no trips were almost entirely in the Group 2 online diary reporting (3.3% and 10.8%,

respectively), compared to the 2006 and 2014 self-reported diary survey results.


62

TABLE 36: NUMBERS OF HOUSEHOLDS, PERSONS AND TRIPS BY SURVEY YEAR (WEIGHTED)

2006 SURVEY

2006 WITH GPS-

BASED ADJUST-

MENT

2014 SURVEY

2017 SURVEY TOTAL

2017 SURVEY GROUP 1 RMOVE

2017 SURVEY GROUP 2 ONLINE DIARY

Households 1,375,702 1,375,702 1,462,107 1,581,620 248,721 1,332,898

Persons 3,267,043 3,267,043 3,537,800 3,850,151 541,115 3,309,037

Persons/HH 2.37 2.37 2.42 2.43 2.18 2.48

Trips 12,273,532 14,762,567 13,782,059 15,432,230 2,677,907 12,754,413

Trips/household 8.92 10.73 9.43 9.76 10.77 9.57

Trips/person 3.76 4.52 3.90 4.01 4.95 3.85

HH with no trips 6.0% 3.6% 3.3% <0.1% 3.3%

Persons with no trips 10.7% 12.2% 10.8% <0.1% 10.8%

Household weekday weighted trip rates were analyzed by household income and participation group.

With increased household income, the trip rate continuously increased from 5.6 trips per day for the

lowest income households (under $25,000) to 14.6 trips per day for households in the highest income

category ($150,000 or more) (Table 37). Of the 3,277 complete households, 110 were found to have

made zero trips, with nearly 100% (109 of these 110 households) in Group 2 (online survey). This is due

to the GPS passive smartphone data collection aspect of Group 1 participation in which RSG’s rMove app

leverages automatic capture of even the shortest of trips, which are routinely under-reported in self-report

online travel surveys. Due to Group 1 having completed 7 days of travel participation, the 705 households

of Group 1 provided 2,188 weekday (Monday – Thursday) complete travel days. The Group 1 households

were only 20% of the target sample however, so the total trips collected were over double at 35,204 trips

for Group 1 vs. 17,469 trips for Group 2 with only 1 day of travel.

TABLE 37: HOUSEHOLD WEEKDAY TRIP RATES BY INCOME

HHs HHs

WEIGHTED TOTAL TRIPS

WEEKDAY TRIPS

WEEKDAY TRIPS

WEIGHTED

WEEKDAY HH

TRIP RATE WEIGHTED

Under $25,000 377 224,229 3,888 2,423 1,250,285 5.6

$25,000 to $49,999 551 276,891 6,639 4,220 2,285,233 8.3

$50,000 to $74,999 524 268,979 8,357 4,967 2,832,840 10.5

$75,000 to $99,999 418 219,640 7,746 4,868 2,147,652 9.8

$100,000 to $149,999 765 286,073 12,602 7,850 2,461,500 8.6

$150,000 or more 642 305,805 13,261 8,202 4,454,811 14.6

TOTAL 3,277 1,581,620 52,493 32,530 15,432,320 9.76

Weighted trip rates by household size are shown in Table 38, with weighted trip rates by age group in

Table 39 and employment status in Table 40.

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TABLE 38: HOUSEHOLD TRIP RATES BY HOUSEHOLD SIZE (GROUPS 1 & 2)

HHs HHs


WEEKDAY TRIPS

TRIPS WEIGHTED

WEEKDAY HH

TRIP RATE WEIGHTED

1 person 1,308 442,708 13,225 8,230 2,106,680 4.8

2 people 1,341 551,280 23,226 14,042 4,524,722 8.2

3 people 337 250,894 7,743 4,663 2,339,842 9.3

4 people 244 201,552 7,173 4,638 3,222,280 16.0

5 or more people 47 135,186 1,126 957 3,238,896 24.0

TOTAL 3,277 1,581,620 52,493 32,530 15,432,320 9.76

TABLE 39: PERSON TRIP RATES BY AGE GROUP

PERSONS PERSONS


WEEKDAY TRIPS

TRIPS WEIGHTED

WEEKDAY PERSON

TRIP RATE

Under 5 years

347 245,257 2,151 1,284 1,139,200 4.64

5-11 years 307 354,152 1,991 1,273 1,408,117 3.98

12-15 years 123 176,759 696 484 491,940 2.78

16-17 years 74 95,591 376 266 250,821 2.62

18-24 years 370 275,843 3,300 1,816 828,898 3.00

25-34 years 1712 616,638 17,585 9,904 2,233,576 3.62

35-44 years 1147 526,274 10,702 6,580 2,378,996 4.52

45-54 years 690 517,750 6,452 4,044 2,251,484 4.35

55-64 years 674 506,070 5,153 3,510 2,249,596 4.45

65-74 years 551 443,974 3,247 2,558 1,861,862 4.19

75-84 years 202 75,980 755 726 291,358 3.83

85 years or older

38 15,862 86 85 46,472 2.93

TOTAL 6,235 3,850,152 52,493 32,530 15,432,320 4.01


64

TABLE 40: PERSON TRIP RATES BY EMPLOYMENT STATUS

PERSONS PERSONS WEIGHTED

TOTAL TRIPS

WEEKDAY TRIPS

TRIPS WEIGHTED

WEEKDAY PERSON

TRIP RATE

Employed full-time (35+ hours/week, paid)

3,084 1,570,289 30,311 17,823 6,057,799 3.86

Employed part-time (fewer than 35 hours/week, paid)

439 322,733 4,413 2,572 1,258,755 3.90

Self-employed 344 115,621 3,208 2,160 491,087 4.25

Unpaid volunteer or intern

52 25,844 445 288 102,653 3.97

Homemaker 275 237,314 2,186 1,433 1,539,966 6.49

Retired 757 480,481 4,041 3,298 2,122,001 4.42

Not currently employed 433 226,109 2,675 1,649 569,982 2.52

Persons <Age 18 851 871,760 5,214 3,307 3,290,079 3.77

TOTAL (Persons) 6,235 3,850,152 52,493 32,530 15,432,321 4.01

AT HOME ACTIVITIES

The study asked questions about online shopping and telework for each day, which are analyzed in

Figure 14 to Figure 17. Because these distributions are so heavily skewed to the low end of the spectrum,

two charts are provided for each topic. The left-most charts show the

percentage of people reporting no time doing these activities, while the right-

most charts show the distribution for people reporting any time doing these

activities. Additionally, these charts refer to the weighted data, and thus reflect

activity on four-weekday days for Group 1 households (Monday-Thursday,

Figure 14 and Figure 16) and three weekdays for Group 2 households

(Tuesday-Thursday, Figure 15 and Figure 17).

Across the Group 1 and Group 2 weekdays, 15-34% of participants report some level of online shopping

activity. The distribution of online shopping time is similar across days with an overall decrease in time

Monday through Thursday and a peak on Friday.

15-34% of

participants report

some level of online

shopping activity

each weekday.

65

FIGURE 14: ONLINE SHOP TIME BY DAY OF WEEK, WEIGHTED (GROUP 1)

FIGURE 15: ONLINE SHOP TIME BY DAY OF WEEK, WEIGHTED (GROUP 2)

Across the Group 1 and Group 2 weekdays, 14-34% of participants

reported some amount of teleworking. Among survey participants who

reported teleworking, the most frequent telework days were Monday,

Tuesday, and Wednesday for Group 1 households, and Wednesday for

Group 2 households. Across all days, the most frequent lengths of

telework time per day were short intervals (e.g., 15-120 minutes), full

work days of eight hours, or 10+ hours (apparent in the Group 1

smartphone households).

14-34% of participants

reported some amount

of teleworking on

weekdays


66

FIGURE 16: TELEWORK TIME BY DAY OF WEEK, WEIGHTED (GROUP 1)

FIGURE 17: TELEWORK TIME BY DAY OF WEEK, WEIGHTED (GROUP 2)

TRAVEL ANALYSIS

The 2017 PSRC travel study resulted in several notable and consistent patterns across all trips (e.g.,

“Home” was the most common trip purpose and “Personal Vehicle” was the most common trip mode).

Some patterns are only recognized when trips are categorized by household size, income, location, and

other variables. The following sections demonstrate these high-level patterns. The results are weighted

trip data for all complete household travel days (days for which there is with 100% complete data) by both

participation modes (Group 1 and Group 2).

In addition, this dataset is a primary input to future transportation modeling at PSRC. Certain figures

reported here, such as travel mode shares, may differ from future travel demand modeling scenarios.

Transit and Taxi/ “TNC” ride share (i.e., “Uber”/ “Lyft”) mode shares are likely to be different between this

report and future modeling scenarios. This dataset reflects the travel of residents of the Puget Sound

67

four-county region and does not necessarily reflect the travel of visitors or nonresidents. The most

common trip destination purpose was to go home, with 34% of all trips overall (Figure 18), a finding that

was consistent across household income levels (Figure 19). Outside of trips to home, “errand/other” were

the most frequent trip types among most income brackets except for the lowest and highest income

brackets (under $24,999 or above $150,000 household income), which had a higher share of “shopping”

trips (Figure 19).

FIGURE 18: TRIP DESTINATION PURPOSE (WEIGHTED)

FIGURE 19: TRIP DESTINATION PURPOSE BY HOUSEHOLD INCOME (WEIGHTED)

Examining the trips by mode, car trips are the most frequent mode, with an 81% mode share overall,

followed by walking (10%), transit (5.5%), and all other modes combined at roughly 3.5%. Looking at


68

variations by income, participants from low-income households (under $24,999 in annual household

income) were much more likely to walk and take transit than the overall population (Figure 20).

FIGURE 20: TRIP MODE BY HOUSEHOLD INCOME (WEIGHTED)

Trip mode shares vary slightly by age, with younger populations having fewer car trips. People 18-24

years old are slightly more likely to use transit and bike, with the largest gains coming from increased

transit usage (Figure 21). Similarly, transit usage slightly spikes for older populations, to around 5% for

those 65-74 and 85 years or older. Otherwise, car usage is mostly stable at around 80% across age

groups. Taxi usage, while a small share overall at less than 1%, also shows a skewed age distribution

toward the young. This is likely due to the fact that 86% of taxi trips used the relatively new smartphone-

based ride-hailing services, such as Uber or Lyft, whose customer bases appear to be much younger

than the overall population. The relatively high proportion of trips by other modes for children under 18

can be mostly accounted for by school bus trips, while the spike in other modes for trips taken by the over

85 years of age category is an anomaly due to a sample size of one and a high weight for this category.

Since the over 85 years of age category is not typically used in planning or modeling studies as a

separate category, this does not present an issue.

69

FIGURE 21: TRIP MODE BY AGE GROUP(WEIGHTED)

Analyzing trip purposes by departure time of the trip shows travel patterns daily life. As shown in Figure

22 of overall travel departure times by hour of day, there were two general peaks over the day of morning

rush hour 8-11 a.m., and then evening 3-6 p.m. Figure 23 represents trips by purpose and hour of day.

Some trip purposes are more evenly distributed throughout the daylight hours, while others followed more

distinct or spiked travel patterns. Most work and school trips began early in the morning, whereas most

home trips occurred in the afternoon and evening. “Escort” trips peaked in mornings and afternoons,

presumably to accompany children or household companions to school or work trips. Shopping trips

peaked in late morning, while errand/other trips had two peaks in the morning and afternoon.

FIGURE 22: TRIPS BY TIME OF DAY (WEIGHTED)


70

FIGURE 23: TRIPS BY TIME OF DAY AND DESTINATION PURPOSE (WEIGHTED)

Trip origins and destinations, and travel distances, were automatically

recorded and calculated for the Group 1 rMove smartphone households.

For the Group 2 online survey households, participants utilized a mapping

interface to enter addresses or click on the map to enter locations, which

were then calculated for all trip distances. In total for both participation

modes, over 500,000 miles of trips were captured by the 3,277 complete

households with most frequent trips under 1 mile (28%) and 11+ miles

(17%) (Figure 24).

When segmented by trip mode, the distributions of trip duration and distance remain consistent across

modes. “Walk” trips are one of the few exceptions in both cases, primarily because most individuals opted

for another mode (other than walking) once the trip reached a certain distance. The disproportionate

number of walk trips under one mile (86%) was also likely a result of many people walking to the location

of their next mode (e.g., walking to a bus stop). Transit trips had a significant peak in 11+ miles trips

(32%) due to long distance express bus and commuter rail service in the PSRC 4-county region (Figure

25).

Over 500,000 miles

of trips were

captured in the 2017

PSRC travel study.

71

FIGURE 24: TRIPS BY DISTANCE (WEIGHTED)

FIGURE 25: TRIPS BY DISTANCE AND TRIP MODE (WEIGHTED)


72

8.0 FUTURE CONSIDERATIONS

8.1 | SUMMARY

The study obtained 3,277 complete households, exceeding the project goal of 3,100 complete

households. These households completed 52,493 trips, totaling over 500,000 miles of travel.

The 2017 PSRC study yielding the 52,493 trips compares favorably to the 2014 study which yielded a

similar volume of ~50,000 trips with an 85% larger sample size in 2014. With the inclusion of the Group 1

rMove passive seven-day smartphone participation sample, the 2017 dataset collected more than 4

million GPS points to detail the origins, destinations, and travel paths of trips. These Group 1 households

were only 20% of the target sample for the 2017 travel study but the total trips collected were double for

Group 1 with 7 days of travel (35,024 trips) compared to Group 2 with only 1 day of self-reported travel

(17,469 trips).

Across multiple demographic measures, the data collected were representative after RSG applied a

rigorous data weighting process using data from the ACS.

8.2 | FUTURE CONSIDERATIONS

As part of the project conclusion, PSRC and the cities of Seattle and Redmond met with RSG to discuss

lessons learned. The three agencies planned to meet at key points in the future to further review data

needs and planning elements of the travel survey program such as revisiting the “ideal” increment and

size of each wave of data collection. Other lessons learned included interest in an earlier contract date,

so that more than 1-2 months would be available to the project team prior to data collection. Smaller

details such as question modifications and sample targets would be covered within any future contracts.

Many of the themes related to lessons learned emerged from the fact that there is a growing set of

stakeholders for the data and meeting those various needs can require additional planning and input over

the course of the project.

73

9.0 APPENDICES

9.1 | QUESTIONNAIRES

(See separate PDF files.)

9.2 | INVITATION MATERIALS

(See separate PDF files.)